{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8986ea10-976f-4521-8be5-dcc9a39eed90",
   "metadata": {
    "tags": []
   },
   "source": [
    "# AutoICE - Future of Automatic Sea Ice Mapping \n",
    "This notebook will introduce you to the thematics and help explore and analyze the input and output data of the AutoICE challenge. It should be seen as a supplement to the challenge dataset manual and repeats some aspects. Two other notebooks are available, the quickstart and test_upload. The quickstart notebook contains an example of a pipeline to load the ready-to-train dataset, as well as training and testing of a 'simple' U-Net model. The test_upload notebook creates ice charts from the test scenes and compiles the output into a NetCDF file ready to upload to AI4EO.eu.\n",
    "\n",
    "## Challenge context\n",
    "Synthetic Aperture Radar (SAR) satellite images are used extensively for producing sea ice charts in support of Arctic navigation. However, due to ambiguities in the relationship between SAR backscatter and ice conditions (different ice types and concentrations, as well as different wind conditions over the ocean, have the same backscatter signature), the process of producing ice charts is done by manual interpretation of the satellite data taking into account also the texture patterns of the ice in the SAR images. The process is labour intensive and time-consuming, and thus, the amount of ice charts that are produced on a given day is limited. Automatically generated high-resolution sea ice maps have the potential to increase the use of satellite imagery in ice charting by providing more products and shorter time delays between acquisition and product availability. The design of an automatic and robust sea ice classification scheme has been studied for many years. Recent approaches to this issue that use Convolutional Neural Networks (i.e. image segmentation techniques) show promising results. This challenge aims to create AI systems that can exploit the spatial information of Sentinel-1 SAR images to map sea ice in the Arctic.\n",
    "\n",
    "## Objective\n",
    "Your task will be to map three sea ice parameters, the total Sea Ice Concentration (SIC), the Stage Of Development (SOD), and the floe size (FLOE). To successfully map these sea ice parameters, the AI4Arctic sea ice challenge dataset has been prepared. The AI4Arctic sea ice challenge dataset includes 493 training and 20 testing files in netCDF format. Each file contains: \n",
    "two-channel dual polarized (HH and HV) Sentinel-1 Extra Wide Swath (EW) images, auxiliary Sentinel-1 image parameters, microwave radiometer measurements from the AMSR2 sensor on board the JAXA GCOM-W satellite,\n",
    "several Numerical Weather Prediction (NWP) parameters from the ERA5 reanalysis dataset, the corresponding ice chart based on that Sentinel-1 image, produced by either the Greenland Ice Service at DMI or the Canadian Ice Service (CIS).\n",
    "\n",
    "Two versions of the dataset are available, the raw version and the ready-to-train version, to allow for both full experimentability and to quickly get started. In this notebook, only the ready-to-train version will be examined. The challenge dataset manual contains a more detailed explanation of the dataset. The dataset contains the following data variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ee9d147",
   "metadata": {},
   "outputs": [
    {
     "data": {
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AuAw/Pz9NnTpVjz/+eIl90dHROnXqlJYuXVq0rWPHjgoNDdWsWbPsPofxW5hiY2OVlpYmSdq/f79iYmJUvXp1LVy4UMOHD7/i93Nzc5WTk1PsxQqlAAAAcEVX+7Ntfn6+5s2bp1OnTiksLOyyn9myZYt69OhRbFvPnj21ZcsWhzIan0Sdlpam0NBQSefvyeratavmzp2rzZs3KyYmRomJiWV+Pz4+XhMmTCi27YHYOD349F/LKXHlEBnSwHQEAABQjryN/5RWdV3Xdoixc798f90SP9uOGzdO48ePv+znU1NTFRYWprNnz6pmzZr67LPPipZIuFRGRob8/f2LbfP391dGRoZDGY3/0ywsLFRBQYGk849xjYyMlCQ1atRImZmZV/z+iBEjFBcXV2zbsu+znB8UAAAAKGeX+9nWy8ur1M8HBQVp9+7dys7O1qJFizRo0CBt2LCh1CbCGYw3EO3atdNrr72mHj16aMOGDZo5c6ak8wvMXdohXY6Xl1eJv1TPaqfKJSsAAABQni73s21ZqlWrpsDAQEnS7bffrh07dujNN9/UO++8U+Kz9evX15EjR4ptO3LkiOrXr+9QRuNzIBITE5WcnKwhQ4Zo1KhRRX8BixYtUqdOnQynAwAAQJVjczP3ukYFBQWlzpkICwvT2rVri2374osvSp0zURrjIxBt2rRRampqie1Tp06Vu7u7gUQAAABA5TdixAj17t1bjRs31smTJzV37lytX79eq1atkiQNHDhQAQEBio+PlyS98MIL6tatm9544w3de++9mjdvnv73v//p3Xffdei8xhuI0nh7e5uOAAAAgKrIZjOdwC5Hjx7VwIEDdfjwYfn4+KhNmzZatWqV7rnnHknSwYMH5eb2/0Y1OnXqpLlz52r06NEaOXKkmjdvriVLlji0BoRUCdaByM/PV0JCghYsWKCDBw8qLy+v2P6sLMcnRC/ac9hZ8SotnsIEAIC18RQmc667/QVj5z6z801j57aX8TkQEyZM0LRp0xQdHa3s7GzFxcWpX79+cnNzK/VxVQAAAADMMN5AzJkzR++9956GDh0qDw8P9e/fX7Nnz9bYsWO1detW0/EAAABQ1bjwJOqKYDxlRkaGWrduLUmqWbOmsrOzJUmRkZFatmyZyWgAAAAALmG8gWjYsKEOHz4/Z6FZs2ZavXq1JGnHjh0OPQMXAAAAcAqbzdzLBRhvIPr27Vv0PNrnnntOY8aMUfPmzTVw4EA99thjhtMBAAAAuJjx+f2TJk0q+nN0dLQaN26sLVu2qHnz5urTp4/BZAAAAKiSXGQuginGG4hLhYWFObwaHgAAAICKYaSB+Pzzz+3+7H333VeOSQAAAAA4wkgDERUVZdfnbDab8vPzyzcMAAAAcDEXmcxsipEGoqCgwMRpAQAAAFyjSjcHAgAAADCKSdRlMva3s27dOgUHBysnJ6fEvuzsbIWEhGjjxo0GkgEAAAAojbEGIjExUYMHD1atWrVK7PPx8VFsbKwSEhIMJAMAAABQGmMNxJ49e9SrV69S94eHh2vnzp0VmAgAAAAQK1FfgbEG4siRI/L09Cx1v4eHh44dO1aBiQAAAABcibEGIiAgQHv37i11f0pKiho0aFCBiQAAAACdn0Rt6uUCjKWMiIjQmDFjdPbs2RL7zpw5o3HjxikyMtJAMvulf7NH/5o0QpNi/6xRD3bXN9v/azqS082bO0e97/mT2rdtrYdiHlBqSorpSE5n9Rqpz/VZvUar1ydZv0bqA6oWYw3E6NGjlZWVpVtuuUVTpkxRUlKSkpKSNHnyZAUFBSkrK0ujRo0yFc8uebln1eCmZurz+Iumo5SLlSuW6/Up8Yp95lnNW/iZgoJa6OnYx3X8+HHT0ZzG6jVSn+uzeo1Wr0+yfo3UB0tiDkSZjDUQ/v7++uqrr9SqVSuNGDFCffv2Vd++fTVy5Ei1atVKmzZtkr+/v6l4dglqe4fuiXlCIR3uNB2lXHz80Qfq95cHFdX3z2oWGKjR4ybI29tbS/692HQ0p7F6jdTn+qxeo9Xrk6xfI/UBVY/RG62aNGmi5cuXKzMzU9u2bdPWrVuVmZmp5cuXq2nTpiajVXl/5OXp22++VsewTkXb3Nzc1LFjJ6Xs2WUwmfNYvUbqc31Wr9Hq9UnWr5H6gKqpUszU8PX1Vfv27dWhQwf5+vqajgNJJ347ofz8fNWpU6fY9jp16igzM9NQKueyeo3U5/qsXqPV65OsXyP1wbKYRF0m10gJAAAAoFLwMB0AlZNvbV+5u7uXmCR2/Phx1a1b11Aq57J6jdTn+qxeo9Xrk6xfI/XBslxkMrMpjEDgsjyrVVPL4BBt27qlaFtBQYG2bduiNre2NZjMeaxeI/W5PqvXaPX6JOvXSH1A1cQIxDXIPXtaxzN+LXp/4miGDv30g6rXrKXadSv3E6Ts8fCgRzVm5MsKCWmlVq3b6JOPP9KZM2cU1bef6WhOY/Uaqc/1Wb1Gq9cnWb9G6gOqHhqIa/Drj9/r/QkvFb1f/q8ZkqS23XrqL8+OMBXLaXr1jtCJrCy9Pf0tZWYeU1CLlnr7ndmqY6FhW6vXSH2uz+o1Wr0+yfo1Uh8syUUmM5tiKywsLDQdwtkW7TlsOkK5iwxpYDoCAAAoR978mteY67qON3buMxvNndtelba9OnLkiF555RXTMQAAAFDV8BjXMlXalBkZGZowYYLpGAAAAAAuYmxwLCUlpcz933//fQUlAQAAAC7ixmNcy2KsgQgNDZXNZtPlpmBc2G7jGbwAAABApWKsgfDz89OUKVN09913X3b/119/rT59+lRwKgAAAABlMdZA3H777Tp06JCaNGly2f2//fbbZUcnAAAAgHLlIpOZTTHWQDz11FM6depUqfsbN26sDz74oAITAQAAALgSYw1E3759y9zv6+urQYMGVVAaAAAA4P/HPNwyMT4DAAAAwG40EAAAAADsxiLpAAAAwMWYRF0m/nYAAAAA2I0RCAAAAOBiTKIuk/ERiJUrV2rTpk1F72fMmKHQ0FANGDBAJ06cMJgMAAAAwKWMNxDDhg1TTk6OJCk1NVVDhw5VRESE0tPTFRcXZzgdAAAAqhybm7mXCzB+C1N6erqCg4MlSYsXL1ZkZKQmTpyo5ORkRUREGE4HAAAA4GLG25xq1arp9OnTkqQ1a9YoPDxckuTn51c0MgEAAACgcjA+AtGlSxfFxcWpc+fO2r59u+bPny9JSktLU8OGDQ2nAwAAQJXDJOoyGR+BmD59ujw8PLRo0SLNnDlTAQEBkqQVK1aoV69ehtMBAAAAuJjxEYjGjRtr6dKlJbYnJCQYSAMAAIAqz0UmM5ti/G8nOTlZqampRe+TkpIUFRWlkSNHKi8vz2AyAAAAAJcy3kDExsYqLS1NkrR//37FxMSoevXqWrhwoYYPH37F7+fm5ionJ6fY64+83PKODQAAAFRJxm9hSktLU2hoqCRp4cKF6tq1q+bOnavNmzcrJiZGiYmJZX4/Pj5eEyZMKLZt1JhxGj12fPkEriR82w8xHaFcndgx3XQEAABQVTGJukzGG4jCwkIVFBRIOv8Y18jISElSo0aNlJmZecXvjxgxosSCc4XuXs4PCgAAAMB8A9GuXTu99tpr6tGjhzZs2KCZM2dKOr/AnL+//xW/7+XlJS+v4g3D2XPlEhUAAABVAZOoy2T8bycxMVHJyckaMmSIRo0apcDAQEnSokWL1KlTJ8PpAAAAAFzM+AhEmzZtij2F6YKpU6fK3d3dQCIAAAAApTHeQJTG29vbdAQAAABURUyiLpPxBiI/P18JCQlasGCBDh48WGLth6ysLEPJAAAAAFzK+ByICRMmaNq0aYqOjlZ2drbi4uLUr18/ubm5afz48abjAQAAoKqxuZl7uQDjKefMmaP33ntPQ4cOlYeHh/r376/Zs2dr7Nix2rp1q+l4AAAAAC5ivIHIyMhQ69atJUk1a9ZUdna2JCkyMlLLli0zGQ0AAABVESMQZTKesmHDhjp8+LAkqVmzZlq9erUkaceOHSXWdwAAAABglvEGom/fvlq7dq0k6bnnntOYMWPUvHlzDRw4UI899pjhdAAAAAAuZvwpTJMmTSr6c3R0tBo3bqwtW7aoefPm6tOnj8FkAAAAqJJ4jGuZjDcQlwoLC1NYWJjpGAAAAAAuw0gD8fnnn9v92fvuu68ckwAAAACXcJHJzKYYaSCioqLs+pzNZlN+fn75hgEAAABgNyMNREFBgYnTAgAAALhGlW4OBAAAAGAUk6jLZOwGr3Xr1ik4OFg5OTkl9mVnZyskJEQbN240kAwAAABAaYw1EImJiRo8eLBq1apVYp+Pj49iY2OVkJBgIBkAAACqNFaiLpOxlHv27FGvXr1K3R8eHq6dO3dWYCIAAAAAV2JsDsSRI0fk6elZ6n4PDw8dO3asAhMBAAAAYg7EFRgbgQgICNDevXtL3Z+SkqIGDRpUYCIAAAAAV2KsgYiIiNCYMWN09uzZEvvOnDmjcePGKTIy0kAyx8ybO0e97/mT2rdtrYdiHlBqSorpSE4z+IEu2j5/hI78d6qO/Heq1n80VOGdg03HcjorX0OJ+qzA6jVavT7J+jVSH1C1GGsgRo8eraysLN1yyy2aMmWKkpKSlJSUpMmTJysoKEhZWVkaNWqUqXh2WbliuV6fEq/YZ57VvIWfKSiohZ6OfVzHjx83Hc0pfj3ym8b8I0mdHpqizg9N1frtaVqY8KRa3lzfdDSnsfo1pD7XZ/UarV6fZP0aqQ9WZLPZjL1cga2wsLDQ1MkPHDigp59+WqtWrdKFGDabTT179tSMGTPUtGnTqzru2XPOTFm6h2IeUEir1ho5eqyk8wvkhd/dTf0HPKzHBz9Zruf2bT+kXI9fml/XT9bIxCX6aMmWcj3PiR3Ty/X4F5i8hhWB+lyf1Wu0en2S9WukvvLjzWpdxlT/8z+Nnfv04seMndteRp8V1aRJEy1fvlyZmZnatm2btm7dqszMTC1fvvyqm4eK8kdenr795mt1DOtUtM3NzU0dO3ZSyp5dBpOVDzc3mx7oebtqXFdN21LSTcdxCqtfQ+pzfVav0er1SdavkfpgVYxAlK1S9La+vr5q37696RgOOfHbCeXn56tOnTrFttepU0fp6fsNpXK+kMAbtf6jofKu5qHfz+Qqeuh7+m5/hulYTmH1a0h9rs/qNVq9Psn6NVIfUDVVigYClVfaT0d0R0y8fGpep7492uq9Vx5W+BNvWqaJAAAAgGNoIK6Sb21fubu7l5hEdfz4cdWtW9dQKuf741y+9v+cKUna9e3Puj2ksZ7t313P/X2e4WTXzurXkPpcn9VrtHp9kvVrpD5YlmvcSWSMa6yXXQl5VqumlsEh2rb1/00mLigo0LZtW9Tm1rYGk5UvN5tNXtWs0Xda/RpSn+uzeo1Wr0+yfo3UB1RN1vhJ0JCHBz2qMSNfVkhIK7Vq3UaffPyRzpw5o6i+/UxHc4pXnrtPqzZ/rZ8Pn9D1NbwV3budurZrrj7PvG06mtNY/RpSn+uzeo1Wr0+yfo3UBytylcnMptBAXINevSN0IitLb09/S5mZxxTUoqXefme26lhkWPMGv5p6/9WBql+3lrJ/P6u9P/yqPs+8rXXbvjMdzWmsfg2pz/VZvUar1ydZv0bqA6oeo+tAlJeKWgfCJFPrQFSUiloHAgCAyop1IMy5PvojY+c+OX+QsXPby/gciF9++UW///57ie1//PGHNm7caCARAAAAgNIYayAOHz6sDh06qEmTJqpdu7YGDhxYrJHIysrSXXfdZSoeAAAAgMsw1kD87W9/k5ubm7Zt26aVK1fqm2++0V133aUTJ04UfcaCd1cBAACgkmMl6rIZayDWrFmjt956S+3atVOPHj20efNmNWjQQH/605+UlZUliRnwAAAAQGVjrIHIzs6Wr69v0XsvLy/9+9//1k033aS77rpLR48eNRUNAAAAVRgjEGUz1kDcfPPNSklJKbbNw8NDCxcu1M0336zIyEhDyQAAAACUxlgD0bt3b7377rsltl9oIkJDQys+FAAAAIAyGXvC8N///nedPn36svs8PDy0ePFi/frrrxWcCgAAAFWea9xJZIyxEQgPDw/VqlWrzP1NmjSpwEQAAAAArsT4QnIAAABAZeIqk6jj4+PVvn17XX/99apXr56ioqL0/fffl/mdDz/8sMQ5vb29HTovDQQAAADggjZs2KBnn31WW7du1RdffKE//vhD4eHhOnXqVJnfq1Wrlg4fPlz0OnDggEPnNTYHAgAAAKiMXOVxqitXriz2/sMPP1S9evW0c+dOde3atdTv2Ww21a9f/6rPywgEAAAAUEnk5uYqJyen2Cs3N9eu72ZnZ0uS/Pz8yvzc77//riZNmqhRo0a6//779fXXXzuU0XgDsXLlSm3atKno/YwZMxQaGqoBAwboxIkTBpMBAAAAFSs+Pl4+Pj7FXvHx8Vf8XkFBgV588UV17txZrVq1KvVzQUFB+uc//6mkpCR98sknKigoUKdOnfTLL7/YndFWWFhYaPeny0Hr1q01efJkRUREKDU1Ve3bt1dcXJy+/PJLtWjRQh988IHDxzx7rhyCVjK+7YeYjlCuTuyYbjoCAABGeXOjuTF+D881du7Ds/9cYsTBy8tLXl5eZX7v6aef1ooVK7Rp0yY1bNjQ7vP98ccfatmypfr3769XX33Vru8Y/6eZnp6u4OBgSdLixYsVGRmpiRMnKjk5WREREYbTAQAAABXHnmbhUkOGDNHSpUu1ceNGh5oHSfL09FTbtm21b98+u79j/BamatWqFS0ot2bNGoWHh0s6f+9WTk6OyWgAAACoglzlMa6FhYUaMmSIPvvsM61bt05NmzZ1uNb8/HylpqaqQYMGdn/H+AhEly5dFBcXp86dO2v79u2aP3++JCktLc3hDgoAAACoKp599lnNnTtXSUlJuv7665WRkSFJ8vHx0XXXXSdJGjhwoAICAormUbzyyivq2LGjAgMD9dtvv2nq1Kk6cOCAnnjiCbvPa3wEYvr06fLw8NCiRYs0c+ZMBQQESJJWrFihXr16GU4HAAAAVE4zZ85Udna2unfvrgYNGhS9LvxCXpIOHjyow4cPF70/ceKEBg8erJYtWyoiIkI5OTn66quviqYU2MP4JOrywCRq18ckagBAVcckanPqDPrU2LmPf9Tf2LntZXwEIjk5WampqUXvk5KSFBUVpZEjRyovL89gMgAAAACXMt5AxMbGKi0tTZK0f/9+xcTEqHr16lq4cKGGDx9+xe9fy2IbAAAAwKVcZRK1KcYHx9LS0hQaGipJWrhwobp27aq5c+dq8+bNiomJUWJiYpnfj4+P14QJE4ptGzVmnEaPHV8+gSsJq9/i0/31DaYjlKv1f+1mOgIAAMBVMd5AFBYWqqCgQNL5x7hGRkZKkho1aqTMzMwrfn/EiBGKi4srfkx3x56dCwAAAFzgKiMBphhvINq1a6fXXntNPXr00IYNGzRz5kxJ5xeY8/f3v+L3L7fYRlWYRA0AAACYYHwORGJiopKTkzVkyBCNGjVKgYGBkqRFixapU6dOhtMBAAAAuJjxEYg2bdoUewrTBVOnTpW7u7uBRAAAAKjKuIWpbMYbiNJ4e3ubjgAAAADgEsYbiPz8fCUkJGjBggU6ePBgibUfsrKyDCUDAABAlcQARJmMz4GYMGGCpk2bpujoaGVnZysuLk79+vWTm5ubxo8fbzoeAAAAgIsYbyDmzJmj9957T0OHDpWHh4f69++v2bNna+zYsdq6davpeAAAAAAuYryByMjIUOvWrSVJNWvWVHZ2tiQpMjJSy5YtMxkNAAAAVRArUZfNeAPRsGFDHT58WJLUrFkzrV69WpK0Y8eOEus7AAAAADDLeAPRt29frV27VpL03HPPacyYMWrevLkGDhyoxx57zHA6AAAAVDWMQJTN+FOYJk2aVPTn6OhoNW7cWFu2bFHz5s3Vp08fg8kAAAAAXMp4A3GpsLAwhYWFmY4BAACAKspVRgJMMdJAfP7553Z/9r777ivHJAAAAAAcYaSBiIqKsutzNptN+fn55RsGAAAAgN2MNBAFBQUmTgsAAABcEbcwlc34U5gAAAAAuA5jDcS6desUHBysnJycEvuys7MVEhKijRs3GkgGAACAKs1m8OUCjDUQiYmJGjx4sGrVqlVin4+Pj2JjY5WQkGAgGQAAAIDSGGsg9uzZo169epW6Pzw8XDt37qzARAAAAACuxNg6EEeOHJGnp2ep+z08PHTs2LEKTAQAAAAwifpKjI1ABAQEaO/evaXuT0lJUYMGDSowEQAAAIArMdZAREREaMyYMTp79myJfWfOnNG4ceMUGRlpIJlj5s2do973/Ent27bWQzEPKDUlxXQkp7JyfQM7NtI/B7XV2pc6a/lzYZrcL0SN/a4zHcvprHwNJevXJ1m/RqvXJ1m/RuqD1dhsNmMvV2CsgRg9erSysrJ0yy23aMqUKUpKSlJSUpImT56soKAgZWVladSoUabi2WXliuV6fUq8Yp95VvMWfqagoBZ6OvZxHT9+3HQ0p7B6fW0b19bi5EN64uNden5+ijzcbHozuo28Pa3zdGOrX0Or1ydZv0ar1ydZv0bqA6oeYz8p+fv766uvvlKrVq00YsQI9e3bV3379tXIkSPVqlUrbdq0Sf7+/qbi2eXjjz5Qv788qKi+f1azwECNHjdB3t7eWvLvxaajOYXV63tpQaqWpR5ReuZp7Tt6Sq8u+14NfLzVov71pqM5jdWvodXrk6xfo9Xrk6xfI/UBVY/RX7U2adJEy5cvV2ZmprZt26atW7cqMzNTy5cvV9OmTU1Gu6I/8vL07Tdfq2NYp6Jtbm5u6tixk1L27DKYzDmsXt/l1PRylyTlnPnDcBLnsPo1tHp9kvVrtHp9kvVrpD5YFbcwla1S3Kvh6+ur9u3bq0OHDvL19TUdxy4nfjuh/Px81alTp9j2OnXqKDMz01Aq57F6fZeySXqxR6D2/Jyt/ZmnTcdxCqtfQ6vXJ1m/RqvXJ1m/RuoDqiZjj3EFKpNh4c3V7IYaevITfqMEAECV5xoDAcbQQFwl39q+cnd3LzGJ6vjx46pbt66hVM5j9fouNvSeQHUO9NNTc/bo2Mk803GcxurX0Or1Sdav0er1SdavkfqAqqlS3MLkijyrVVPL4BBt27qlaFtBQYG2bduiNre2NZjMOaxe3wVD7wlUt1vqasinKTqcXfKRwq7M6tfQ6vVJ1q/R6vVJ1q+R+mBVzIEoGyMQ1+DhQY9qzMiXFRLSSq1at9EnH3+kM2fOKKpvP9PRnMLq9Q0LD1R4sL+GL96rU3nn5Ffj/Mrop3LzlXuuwHA657D6NbR6fZL1a7R6fZL1a6Q+oOqhgbgGvXpH6ERWlt6e/pYyM48pqEVLvf3ObNWxyLCm1ev7820BkqSZD4UW2/7qsu+0LPWIgUTOZ/VraPX6JOvXaPX6JOvXSH1A1WMrLCwsNB3C2c6eM50A16r76xtMRyhX6//azXQEAEAl582veY1p8vx/jJ37wFt9jJ3bXkb/aR4/flwpKSm69dZb5efnp8zMTL3//vvKzc3VAw88oJYtW5qMBwAAAOASxhqI7du3Kzw8XDk5Oapdu7a++OILPfDAA/Lw8FBBQYEmTZqkTZs26bbbbjMVEQAAAFWQq0xmNsXYU5hGjRqlBx54QNnZ2Ro5cqSioqJ09913Ky0tTfv27VNMTIxeffVVU/EAAAAAXIaxBmLnzp2Ki4vT9ddfrxdeeEGHDh3S4MGDi/YPGTJEO3bsMBUPAAAAwGUYu4UpLy9P1113nSTJ09NT1atXL7YoS926dUss3AIAAACUN25hKpuxEYhGjRpp//79Re/nzZunBg0aFL0/fPgwqzwCAAAAlYyxEYiYmBgdPXq06P29995bbP/nn3+uDh06VHQsAAAAVHUMQJTJWAMxbty4MvePGjVK7u7uFZQGAAAAgD0q7RIl1atXNx0BAAAAVRBzIMpmbA4EAAAAANdDAwEAAADAbpX2FiYAAADABG5hKhsjEAAAAADsZryBWLlypTZt2lT0fsaMGQoNDdWAAQN04sQJg8kAAABQFdls5l6uwHgDMWzYMOXk5EiSUlNTNXToUEVERCg9PV1xcXGG0wEAAAC4mPE5EOnp6QoODpYkLV68WJGRkZo4caKSk5MVERFhOB0AAACAixlvIKpVq6bTp09LktasWaOBAwdKkvz8/IpGJgAAAICKwiTqshlvILp06aK4uDh17txZ27dv1/z58yVJaWlpatiwoeF0AAAAAC5mfA7E9OnT5eHhoUWLFmnmzJkKCAiQJK1YsUK9evUynA4AAABVDZOoy2Z8BKJx48ZaunRpie0JCQkG0gAAAAAoi/ERiOTkZKWmpha9T0pKUlRUlEaOHKm8vDyDyQAAAFAV2Ww2Yy9XYLyBiI2NVVpamiRp//79iomJUfXq1bVw4UINHz78it/Pzc1VTk5OsVdubm55xwYAAACqJOO3MKWlpSk0NFSStHDhQnXt2lVz587V5s2bFRMTo8TExDK/Hx8frwkTJhTbNmrMOI0eO758AqNCrP9rN9MRylX31zeYjlDurH4NAQCoqow3EIWFhSooKJB0/jGukZGRkqRGjRopMzPzit8fMWJEiQXnCt29nB8UAAAAVYKL3ElkjPEGol27dnrttdfUo0cPbdiwQTNnzpR0foE5f3//K37fy8tLXl7FG4az58olKgAAAFDlGW8gEhMT9dBDD2nJkiUaNWqUAgMDJUmLFi1Sp06dDKcDAABAVePmxhBEWYw3EG3atCn2FKYLpk6dKnd3dwOJAAAAAJTGeANRGm9vb9MRAAAAAFzCeAORn5+vhIQELViwQAcPHiyx9kNWVpahZAAAAKiKmERdNuPrQEyYMEHTpk1TdHS0srOzFRcXp379+snNzU3jx483HQ8AAADARYw3EHPmzNF7772noUOHysPDQ/3799fs2bM1duxYbd261XQ8AAAAVDGsRF024w1ERkaGWrduLUmqWbOmsrOzJUmRkZFatmyZyWgAAAAALmG8gWjYsKEOHz4sSWrWrJlWr14tSdqxY0eJ9R0AAACA8mazmXu5AuMNRN++fbV27VpJ0nPPPacxY8aoefPmGjhwoB577DHD6QAAAABczPhTmCZNmlT05+joaDVu3FhbtmxR8+bN1adPH4PJAAAAAFzKeANxqbCwMIWFhZmOAQAAgCrKVSYzm2Kkgfj888/t/ux9991XjkkAAAAAOMJIAxEVFWXX52w2m/Lz88s3DAAAAHARRiDKZqSBKCgoMHFaAAAAANfI+FOYAAAAALgOYw3EunXrFBwcrJycnBL7srOzFRISoo0bNxpIBgAAgKqMdSDKZqyBSExM1ODBg1WrVq0S+3x8fBQbG6uEhAQDyQAAAACUxlgDsWfPHvXq1avU/eHh4dq5c2cFJgIAAADOT6I29XIFxhqII0eOyNPTs9T9Hh4eOnbsWAUmAgAAAHAlxhqIgIAA7d27t9T9KSkpatCgQQUmAgAAAJgDcSXGGoiIiAiNGTNGZ8+eLbHvzJkzGjdunCIjIw0kc8y8uXPU+54/qX3b1noo5gGlpqSYjuRUVq9Psm6NAzs20j8HtdXalzpr+XNhmtwvRI39rjMdy+msev0uZvUarV6fZP0aqQ+oWow1EKNHj1ZWVpZuueUWTZkyRUlJSUpKStLkyZMVFBSkrKwsjRo1ylQ8u6xcsVyvT4lX7DPPat7CzxQU1EJPxz6u48ePm47mFFavT7J2jW0b19bi5EN64uNden5+ijzcbHozuo28Pa3z9GYrX78LrF6j1euTrF8j9QFVj7GfJPz9/fXVV1+pVatWGjFihPr27au+fftq5MiRatWqlTZt2iR/f39T8ezy8UcfqN9fHlRU3z+rWWCgRo+bIG9vby3592LT0ZzC6vVJ1q7xpQWpWpZ6ROmZp7Xv6Cm9uux7NfDxVov615uO5jRWvn4XWL1Gq9cnWb9G6oMVMYm6bEZ/FdmkSRMtX75cmZmZ2rZtm7Zu3arMzEwtX75cTZs2NRntiv7Iy9O333ytjmGdira5ubmpY8dOStmzy2Ay57B6fVLVqPFiNb3cJUk5Z/4wnMQ5qsL1s3qNVq9Psn6N1AdUTZXiXgZfX1+1b99eHTp0kK+vr+k4djnx2wnl5+erTp06xbbXqVNHmZmZhlI5j9Xrk6pGjRfYJL3YI1B7fs7W/szTpuM4RVW4flav0er1SdavkfpgVUyiLpuH6QAAyt+w8OZqdkMNPfkJvzEDAADXhgbiKvnW9pW7u3uJSVTHjx9X3bp1DaVyHqvXJ1WNGiVp6D2B6hzop6fm7NGxk3mm4zhNVbh+Vq/R6vVJ1q+R+oCqqVLcwuSKPKtVU8vgEG3buqVoW0FBgbZt26I2t7Y1mMw5rF6fVDVqHHpPoLrdUldDPk3R4eySj0x2ZVXh+lm9RqvXJ1m/RuqDVTGJumyMQFyDhwc9qjEjX1ZISCu1at1Gn3z8kc6cOaOovv1MR3MKq9cnWbvGYeGBCg/21/DFe3Uq75z8apxf+f1Ubr5yzxUYTuccVr5+F1i9RqvXJ1m/RuoDqh4aiGvQq3eETmRl6e3pbykz85iCWrTU2+/MVh2LDGtavT7J2jX++bYASdLMh0KLbX912XdalnrEQCLns/L1u8DqNVq9Psn6NVIfrMhFBgKMsRUWFhaaDuFsZ8+ZTgCUrfvrG0xHKHfr/9rNdAQAcGne/JrXmA4T1xs79/aR3Y2d216Vbg7EzTffrB9++MF0DAAAAFRRrjIHIj4+Xu3bt9f111+vevXqKSoqSt9///0Vv7dw4UK1aNFC3t7eat26tZYvX+7QeY31tm+99dZltx88eFAffPCB6tevL0l6/vnnKzIWAAAA4BI2bNigZ599Vu3bt9e5c+c0cuRIhYeH65tvvlGNGjUu+52vvvpK/fv3V3x8vCIjIzV37lxFRUUpOTlZrVq1suu8xm5hcnNzU0BAgDw8ivcwBw4c0I033ihPT0/ZbDbt37/f4WNzCxMqO25hAgBcCbcwmXNHvLn/Tm8bcfX//Tx27Jjq1aunDRs2qGvXrpf9THR0tE6dOqWlS5cWbevYsaNCQ0M1a9Ysu85j7J/mk08+qW3btmnu3Llq2bJl0XZPT0+tXr1awcHBpqIBAACgCjM5iTo3N1e5ubnFtnl5ecnLy+uK383OzpYk+fn5lfqZLVu2KC4urti2nj17asmSJXZnNDYHYtasWRo7dqx69uyp6dOnm4oBAAAAVBrx8fHy8fEp9oqPj7/i9woKCvTiiy+qc+fOZd6KlJGRIX9//2Lb/P39lZGRYXdGo4Njffv2VYcOHTRw4EAtW7ZMH3zwgck4AAAAgNEF3UaMGFFihMCe0Ydnn31We/fu1aZNm8orWhHjd9cFBARozZo1mjRpktq2bSsLPlUWAAAAsIu9tytdbMiQIVq6dKk2btyohg0blvnZ+vXr68iR4utBHTlypOgBRvaoFI9xtdlsGjFihJYuXao33nhDDRo0MB0JAAAAqNQKCws1ZMgQffbZZ1q3bp2aNm16xe+EhYVp7dq1xbZ98cUXCgsLs/u8xkcgLnb77bfr9ttvNx0DAAAAVZirrET97LPPau7cuUpKStL1119fNI/Bx8dH1113nSRp4MCBCggIKJpH8cILL6hbt2564403dO+992revHn63//+p3fffdfu81aKEQgAAAAAjpk5c6ays7PVvXt3NWjQoOg1f/78os8cPHhQhw8fLnrfqVMnzZ07V++++65uvfVWLVq0SEuWLLF7DQipko1AAAAAAKaZnETtCHvmDq9fv77EtgceeEAPPPDAVZ+XEQgAAAAAdqOBAAAAAGA34w3EypUriz2vdsaMGQoNDdWAAQN04sQJg8kAAABQFdls5l6uwHgDMWzYMOXk5EiSUlNTNXToUEVERCg9Pb3EIhoAAAAAzDI+iTo9PV3BwcGSpMWLFysyMlITJ05UcnKyIiIiDKcDAABAVeMqk6hNMT4CUa1aNZ0+fVqStGbNGoWHh0uS/Pz8ikYmAAAAAFQOxkcgunTpori4OHXu3Fnbt28vem5tWlraFZfiBgAAAJyNEYiyGR+BmD59ujw8PLRo0SLNnDlTAQEBkqQVK1aoV69ehtMBAAAAuJjxEYjGjRtr6dKlJbYnJCQYSAMAAACgLMZHIJKTk5Wamlr0PikpSVFRURo5cqTy8vIMJgMAAEBVxGNcy2a8gYiNjVVaWpokaf/+/YqJiVH16tW1cOFCDR8+/Irfz83NVU5OTrFXbm5ueccGAAAAqiTjtzClpaUpNDRUkrRw4UJ17dpVc+fO1ebNmxUTE6PExMQyvx8fH68JEyYU2zZqzDiNHju+fAIDTrD+r91MRyh33V/fYDpCuaoK1xAAqiomUZfNeANRWFiogoICSecf4xoZGSlJatSokTIzM6/4/REjRpRYcK7Q3cv5QQEAAACYbyDatWun1157TT169NCGDRs0c+ZMSecXmPP397/i9728vOTlVbxhOHuuXKICAAAAVZ7xBiIxMVEPPfSQlixZolGjRikwMFCStGjRInXq1MlwOgAAAFQ13MFUNuMNRJs2bYo9hemCqVOnyt3d3UAiAAAAAKUx3kCUxtvb23QEAAAAVEFMoi6b8QYiPz9fCQkJWrBggQ4ePFhi7YesrCxDyQAAAABcyvg6EBMmTNC0adMUHR2t7OxsxcXFqV+/fnJzc9P48eNNxwMAAEAVw0JyZTPeQMyZM0fvvfeehg4dKg8PD/Xv31+zZ8/W2LFjtXXrVtPxAAAAAFzEeAORkZGh1q1bS5Jq1qyp7OxsSVJkZKSWLVtmMhoAAACASxhvIBo2bKjDhw9Lkpo1a6bVq1dLknbs2FFifQcAAACgvLnZbMZersB4A9G3b1+tXbtWkvTcc89pzJgxat68uQYOHKjHHnvMcDoAAAAAFzP+FKZJkyYV/Tk6OlqNGzfWli1b1Lx5c/Xp08dgMgAAAFRFLjIQYIzxBuJSYWFhCgsLMx0DAAAAwGUYaSA+//xzuz973333lWMSAAAAAI4w0kBERUXZ9Tmbzab8/PzyDQMAAABchJWoy2akgSgoKDBxWgAAAADXqNLNgQAAAABMcmMAokzGHuO6bt06BQcHKycnp8S+7OxshYSEaOPGjQaSAQAAACiNsQYiMTFRgwcPVq1atUrs8/HxUWxsrBISEgwkAwAAQFVms9mMvVyBsQZiz5496tWrV6n7w8PDtXPnzgpMBAAAAOBKjDUQR44ckaenZ6n7PTw8dOzYsQpMBAAAAOBKjDUQAQEB2rt3b6n7U1JS1KBBgwpMBAAAAJxfidrUyxUYayAiIiI0ZswYnT17tsS+M2fOaNy4cYqMjDSQzDHz5s5R73v+pPZtW+uhmAeUmpJiOpJTWb0+yfo1Wrm+gR0b6Z+D2mrtS521/LkwTe4XosZ+15mO5XRWvoaS9euTrF8j9QFVi7EGYvTo0crKytItt9yiKVOmKCkpSUlJSZo8ebKCgoKUlZWlUaNGmYpnl5Urluv1KfGKfeZZzVv4mYKCWujp2Md1/Phx09Gcwur1Sdav0er1tW1cW4uTD+mJj3fp+fkp8nCz6c3oNvL2NPZ/bU5n9Wto9fok69dIfbAim8H/uQJj/5X19/fXV199pVatWmnEiBHq27ev+vbtq5EjR6pVq1batGmT/P39TcWzy8cffaB+f3lQUX3/rGaBgRo9boK8vb215N+LTUdzCqvXJ1m/RqvX99KCVC1LPaL0zNPad/SUXl32vRr4eKtF/etNR3Maq19Dq9cnWb9G6gOqHqO/pmvSpImWL1+uzMxMbdu2TVu3blVmZqaWL1+upk2bmox2RX/k5enbb75Wx7BORdvc3NzUsWMnpezZZTCZc1i9Psn6NVq9vsup6eUuSco584fhJM5h9Wto9fok69dIfUDVVCnG+X19fdW+fXt16NBBvr6+puPY5cRvJ5Sfn686deoU216nTh1lZmYaSuU8Vq9Psn6NVq/vUjZJL/YI1J6fs7U/87TpOE5h9Wto9fok69dIfbAqN5u5lyvwMB0AAJxhWHhzNbuhhp78hN8KAgBQnmggrpJvbV+5u7uXmER1/Phx1a1b11Aq57F6fZL1a7R6fRcbek+gOgf66ak5e3TsZJ7pOE5j9Wto9fok69dIfbAqV1kR2pRKcQuTK/KsVk0tg0O0beuWom0FBQXatm2L2tza1mAy57B6fZL1a7R6fRcMvSdQ3W6pqyGfpuhwdsnHQrsyq19Dq9cnWb9G6gOqJkYgrsHDgx7VmJEvKySklVq1bqNPPv5IZ86cUVTffqajOYXV65OsX6PV6xsWHqjwYH8NX7xXp/LOya/G+dXtT+XmK/dcgeF0zmH1a2j1+iTr10h9sCIGIMpGA3ENevWO0ImsLL09/S1lZh5TUIuWevud2apjkWFNq9cnWb9Gq9f359sCJEkzHwottv3VZd9pWeoRA4mcz+rX0Or1SdavkfqAqsdWWFhYaDqEs509ZzoBgO6vbzAdoVyt/2s30xEAWJw3v+Y1Jmr2/4yde8kT7Yyd216V5p9mYWGh1q9fr3379qlBgwbq2bOnPD09TccCAABAFePGPUxlMtZARERE6NNPP5WPj4+ysrIUERGh7du3q27dujp+/LhuueUWbdy4UTfccIOpiAAAAAAuYewpTCtXrlRubq4kafTo0Tp58qR+/PFHHT16VAcOHFCNGjU0duxYU/EAAABQRdls5l6uoFI8xnXdunWKj49X06ZNJUkNGzbU5MmTtWrVKsPJAAAAAFzMaANxYZGOEydOqFmzZsX2BQYG6tChQyZiAQAAACiFU+ZA/Pbbb6pdu7bD33vkkUfk5eWlP/74Q+np6QoJCSnal5GRcVXHBAAAAK4FK1GXzeERiMmTJ2v+/PlF7x988EHVqVNHAQEB2rNnj93HGTRokOrVqycfHx/df//9On36dLH9ixcvVmhoqKPxAAAAAJQjh0cgZs2apTlz5kiSvvjiC33xxRdasWKFFixYoGHDhmn16tV2HeeDDz4oc/+4cePk7u7uaDwAAADgmjAAUTaHG4iMjAw1atRIkrR06VI9+OCDCg8P10033aQ77rjDacFq1KjhtGMBAAAAcA6Hb2Hy9fXVzz//LOn8o1h79Ogh6fxCcPn5+c5NBwAAAFQwN5vN2MsVODwC0a9fPw0YMEDNmzfX8ePH1bt3b0nSrl27FBgY6PSAAAAAACoPhxuIhIQE3XTTTfr55581ZcoU1axZU5J0+PBhPfPMM04PCAAAAKDycLiB8PT01F//+tcS21966SWnBAIAAABMco0bicyxq4H4/PPP7T7gfffd51CAlStXqmbNmurSpYskacaMGXrvvfcUHBysGTNmyNfX16HjAQAAACg/djUQUVFRdh3MZrM5PJF62LBhmjx5siQpNTVVQ4cOVVxcnL788kvFxcVd8XGvAAAAgDOxkFzZ7GogCgoKyi1Aenq6goODJZ1fPC4yMlITJ05UcnKyIiIiyu28AAAAABzn8GNcL3b27NlrDlCtWrWiVajXrFmj8PBwSZKfn59ycnKu+fgAAAAAnMfhBiI/P1+vvvqqAgICVLNmTe3fv1+SNGbMGL3//vsOB+jSpYvi4uL06quvavv27br33nslSWlpaWrYsKHDxwMAAACuhZvN3MsVONxA/P3vf9eHH36oKVOmqFq1akXbW7VqpdmzZzscYPr06fLw8NCiRYs0c+ZMBQQESJJWrFihXr16OXw8AAAAAOXH4ce4/utf/9K7776ru+++W0899VTR9ltvvVXfffedwwEaN26spUuXltiekJDg8LEAAACAa8Uk6rI5PALx66+/XnbF6YKCAv3xxx8OB0hOTlZqamrR+6SkJEVFRWnkyJHKy8tz+HgAAAAAyo/DDURwcLD++9//lti+aNEitW3b1uEAsbGxSktLkyTt379fMTExql69uhYuXKjhw4df8fu5ubnKyckp9srNzXU4BwAAACBJNpu5lytw+BamsWPHatCgQfr1119VUFCgf//73/r+++/1r3/967K3Il1JWlqaQkNDJUkLFy5U165dNXfuXG3evFkxMTFKTEws8/vx8fGaMGFCsW2jxozT6LHjHc4CVJSlXx82HaHcrf9rN9MRypXVr2FkSAPTEQAAlZTDIxD333+//vOf/2jNmjWqUaOGxo4dq2+//Vb/+c9/dM899zgcoLCwsGidiTVr1hSt/dCoUSNlZmZe8fsjRoxQdnZ2sdewl0c4nAMAAADAlTk8AiFJd955p7744gunBGjXrp1ee+019ejRQxs2bNDMmTMlnV9gzt/f/4rf9/LykpeXV7FtZ885JRoAAACqICZRl+2qGghJ+t///qdvv/1W0vl5EbfffvtVHScxMVEPPfSQlixZolGjRhVN0F60aJE6dep0tfEAAAAAlAOHG4hffvlF/fv31+bNm1W7dm1J0m+//aZOnTpp3rx5Di/+1qZNm2JPYbpg6tSpcnd3dzQeAAAAcE1cZUE3UxyeA/HEE0/ojz/+0LfffqusrCxlZWXp22+/VUFBgZ544gmnBfP29panp6fTjgcAAADg2jk8ArFhwwZ99dVXCgoKKtoWFBSkf/zjH7rzzjsdDpCfn6+EhAQtWLBABw8eLLH2Q1ZWlsPHBAAAAFA+HB6BaNSo0WUXjMvPz9eNN97ocIAJEyZo2rRpio6OVnZ2tuLi4tSvXz+5ublp/PjxDh8PAAAAuBY2m83YyxU43EBMnTpVzz33nP73v/8Vbfvf//6nF154Qa+//rrDAebMmaP33ntPQ4cOlYeHh/r376/Zs2dr7Nix2rp1q8PHAwAAAFB+7LqFydfXt1hHdOrUKd1xxx3y8Dj/9XPnzsnDw0OPPfaYoqKiHAqQkZGh1q1bS5Jq1qyp7OxsSVJkZKTGjBnj0LEAAACAa+Ua4wDm2NVAXGk16GvRsGFDHT58WI0bN1azZs20evVq3XbbbdqxY0eJ9R0AAAAAmGVXAzFo0KByC9C3b1+tXbtWd9xxh5577jn93//9n95//30dPHhQL730UrmdFwAAAIDjrnohOUk6e/Zsiacm1apVy6FjTJo0qejP0dHRaty4sbZs2aLmzZurT58+1xIPAAAAcJibi0xmNsXhBuLUqVN6+eWXtWDBAh0/frzE/vz8/GsKFBYWprCwsGs6BgAAAIDy4XADMXz4cH355ZeaOXOmHn74Yc2YMUO//vqr3nnnnWKjCWX5/PPP7T7ffffd52hEAAAA4KoxAFE2hxuI//znP/rXv/6l7t2769FHH9Wdd96pwMBANWnSRHPmzNFDDz10xWPY+6Qmm812zSMaAAAAAJzH4QYiKytLN998s6Tz8x0urBTdpUsXPf3003Ydo6CgwNHTAgAAABXCVRZ0M8XhheRuvvlmpaenS5JatGihBQsWSDo/MlG7dm2nhgMAAABQuTjcQDz66KPas2ePJOlvf/ubZsyYIW9vb7300ksaNmyY3cdZt26dgoODlZOTU2Jfdna2QkJCtHHjRkfjAQAAAChHDt/CdPHaDD169NB3332nnTt3KjAwUG3atLH7OImJiRo8ePBlH/vq4+Oj2NhYJSQkqGvXro5GBAAAAK4adzCVzeERiEs1adJE/fr1k5+fn5588km7v7dnzx716tWr1P3h4eHauXPntcYDAAAA4ETX3EBccPz4cb3//vt2f/7IkSPy9PQsdb+Hh4eOHTvmjGgAAACA3dxsNmMvV+C0BsJRAQEB2rt3b6n7U1JS1KBBgwpMBAAAAOBKjDUQERERGjNmjM6ePVti35kzZzRu3DhFRkYaSOaYeXPnqPc9f1L7tq31UMwDSk1JMR3Jqaxen2TtGtO/2aN/TRqhSbF/1qgHu+ub7f81HcnpuH6uz8rX8AKr10h9QNVirIEYPXq0srKydMstt2jKlClKSkpSUlKSJk+erKCgIGVlZWnUqFGm4tll5Yrlen1KvGKfeVbzFn6moKAWejr2cR0/ftx0NKewen2S9WvMyz2rBjc1U5/HXzQdpVxw/Vyf1a+hZP0aqQ9WZLOZezli48aN6tOnj2688UbZbDYtWbKkzM+vX79eNputxCsjI8Oh89r9FKZ+/fqVuf+3335z6MT+/v766quv9PTTT2vEiBEqLCyUdH7hjp49e2rGjBny9/d36JgV7eOPPlC/vzyoqL5/liSNHjdBGzeu15J/L9bjg+2fUF5ZWb0+yfo1BrW9Q0Ft7zAdo9xw/Vyf1a+hZP0aqQ8w59SpU7r11lv12GOPXfFn9Yt9//33xZ6EWq9ePYfOa3cD4ePjc8X9AwcOdOjkTZo00fLly3XixAnt27dPhYWFat68uXx9fR06jgl/5OXp22++1uODY4u2ubm5qWPHTkrZs8tgMuewen1S1ajRyrh+rq8qXEOr10h9sCpXWYm6d+/e6t27t8Pfq1ev3jUtAG13A/HBBx9c9UmuxNfXV+3bty+345eHE7+dUH5+vurUqVNse506dZSevt9QKuexen1S1ajRyrh+rq8qXEOr10h9gPPl5uYqNze32DYvLy95eXk57RyhoaHKzc1Vq1atNH78eHXu3Nmh7xubAwEAAABURm4GX/Hx8fLx8Sn2io+Pd0pdDRo00KxZs7R48WItXrxYjRo1Uvfu3ZWcnOzQcRxeiRrn+db2lbu7e4lJVMePH1fdunUNpXIeq9cnVY0arYzr5/qqwjW0eo3UBzjfiBEjFBcXV2ybs0YfgoKCFBQUVPS+U6dO+vHHH5WQkKCPP/7Y7uMwAnGVPKtVU8vgEG3buqVoW0FBgbZt26I2t7Y1mMw5rF6fVDVqtDKun+urCtfQ6jVSH+B8Xl5eqlWrVrGXM29fulSHDh20b98+h77DCMQ1eHjQoxoz8mWFhLRSq9Zt9MnHH+nMmTOK6mv/LPjKzOr1SdavMffsaR3P+LXo/YmjGTr00w+qXrOWatet3E85swfXz/VZ/RpK1q+R+mBFrjKJ2hl2797t8OLNNBDXoFfvCJ3IytLb099SZuYxBbVoqbffma06FhnWtHp9kvVr/PXH7/X+hJeK3i//1wxJUttuPfWXZ0eYiuU0XD/XZ/VrKFm/RuoDzPn999+LjR6kp6dr9+7d8vPzU+PGjTVixAj9+uuv+te//iVJSkxMVNOmTRUSEqKzZ89q9uzZWrdunVavXu3QeW2FFxZgKMPnn39u9wHvu+8+hwKUh7PnTCcAyrb068OmI5S7yBDHfpvhaqx+Da1+/QBX4M2veY15Mek7Y+dOvL+F3Z9dv3697rrrrhLbBw0apA8//FCPPPKIfvrpJ61fv16SNGXKFL377rv69ddfVb16dbVp00Zjx4697DHKYlcD4eZm31QJm82m/Px8uz77yy+/yNvbu2gS0n//+1/NmjVLBw8eVJMmTfTss88qLCzMrmNdigYClZ3Vf/iUrP8DqNWvodWvH+AKaCDMcZUGwhS7OoOCggK7XvY2D5L05z//WVu3bpUkJSUlqXv37vr999/VuXNnnT59Wt26ddPSpUuvrioAAAAA5cJYb/v1118rJCRE0vnn3U6cOFEvv/xy0f7p06dr7NixioyMNBURAAAAVZBb1ZlDfVWuqoE4deqUNmzYoIMHDyovL6/Yvueff96+E3t46OTJk5LOT/i4dBnu3r17F2soAAAAAJjncAOxa9cuRURE6PTp0zp16pT8/PyUmZmp6tWrq169enY3EN26ddOnn36qNm3aqG3btlq/fr3atGlTtP/LL79UQECAo/EAAACAa1KVHuN6NRxuIF566SX16dNHs2bNko+Pj7Zu3SpPT0/93//9n1544QW7jzNp0iTdeeedOnTokLp06aJRo0Zpx44datmypb7//nvNnz9fs2bNcjQeAAAAgHLk8ErUu3fv1tChQ+Xm5iZ3d3fl5uaqUaNGmjJlikaOHGn3cVq2bKlt27YpLy9PU6ZM0alTpzRnzhyNHz9e+/bt07x58/TII484Gg8AAAC4Jm42cy9X4PAIhKenZ9FjXevVq6eDBw+qZcuW8vHx0c8//+zQsZo1a6ZPP/1UhYWFOnr0qAoKClS3bl15eno6GgsAAABABXC4gWjbtq127Nih5s2bq1u3bho7dqwyMzP18ccfq1WrVlcVwmazyd/f/6q+CwAAAKDiOHwL08SJE9WgwfkFhv7+97/L19dXTz/9tI4dO6Z3333X6QEBAACAimSzmXu5AodHINq1a1f053r16mnlypVODQQAAACg8mKRdAAAAOAibq4yFGCIww1E06ZNy3w27v79+68pEAAAAIDKy+EG4sUXXyz2/o8//tCuXbu0cuVKDRs2zOEAK1euVM2aNdWlSxdJ0owZM/Tee+8pODhYM2bMkK+vr8PHBAAAAFA+HG4gSlssbsaMGfrf//7ncIBhw4Zp8uTJkqTU1FQNHTpUcXFx+vLLLxUXF6cPPvjA4WMCAAAAV8vhpwxVMU77++ndu7cWL17s8PfS09MVHBwsSVq8eLEiIyM1ceJEzZgxQytWrHBWPAAAAABO4LQGYtGiRfLz83P4e9WqVdPp06clSWvWrFF4eLgkyc/PTzk5Oc6KBwAAANiFx7iW7aoWkrt4EnVhYaEyMjJ07Ngxvf322w4H6NKli+Li4tS5c2dt375d8+fPlySlpaWpYcOGDh8PAAAAQPlxuIG4//77izUQbm5uuuGGG9S9e3e1aNHC4QDTp0/XM888o0WLFmnmzJkKCAiQJK1YsUK9evVy+HgAAADAteAxrmVzuIEYP368UwM0btxYS5cuLbE9ISHBqecBAAAAcO0cngPh7u6uo0ePlth+/Phxubu7OxwgOTlZqampRe+TkpIUFRWlkSNHKi8vz+HjAQAAACg/DjcQhYWFl92em5uratWqORwgNjZWaWlpks4vQhcTE6Pq1atr4cKFGj58+BW/n5ubq5ycnGKv3Nxch3MAAAAAEpOor8TuW5jeeustSZLNZtPs2bNVs2bNon35+fnauHHjVc2BSEtLU2hoqCRp4cKF6tq1q+bOnavNmzcrJiZGiYmJZX4/Pj5eEyZMKLZt1JhxGj12vMNZgIoSGdLAdARcI6tfw+6vbzAdodyt/2s30xEAwCXZ3UBcmJNQWFioWbNmFbtdqVq1arrppps0a9YshwMUFhaqoKBA0vnHuEZGRkqSGjVqpMzMzCt+f8SIEYqLiyt+THcvh3MAAAAAkuTmIiMBptjdQKSnp0uS7rrrLv373/+Wr6+vUwK0a9dOr732mnr06KENGzZo5syZRefz9/e/4ve9vLzk5VW8YTh7zinRAAAAAFzC4acwffnll04NkJiYqIceekhLlizRqFGjFBgYKOn8wnSdOnVy6rkAAAAAXBuHG4g///nP6tChg15++eVi26dMmaIdO3Zo4cKFDh2vTZs2xZ7CdMHUqVOv6qlOAAAAwLVgHYiyOfwUpo0bNyoiIqLE9t69e2vjxo1OCSVJ3t7e8vT0dNrxAAAAAFw7h0cgfv/998s+rtXT01M5OTkOB8jPz1dCQoIWLFiggwcPllj7ISsry+FjAgAAAFeLAYiyOTwC0bp1a82fP7/E9nnz5ik4ONjhABMmTNC0adMUHR2t7OxsxcXFqV+/fnJzc3P6qtcAAAAAro3DIxBjxoxRv3799OOPP+pPf/qTJGnt2rX69NNPHZ7/IElz5szRe++9p3vvvVfjx49X//791axZM7Vp00Zbt27V888/7/AxAQAAgKvFY1zL5vAIRJ8+fbRkyRLt27dPzzzzjIYOHapffvlFa9asUVRUlMMBMjIy1Lp1a0lSzZo1lZ2dLUmKjIzUsmXLHD4eAAAAgPLj8AiEJN1777269957S2zfu3evWrVq5dCxGjZsqMOHD6tx48Zq1qyZVq9erdtuu007duwosb4DAAAAALMcHoG41MmTJ/Xuu++qQ4cOuvXWWx3+ft++fbV27VpJ0nPPPacxY8aoefPmGjhwoB577LFrjQcAAAA4xGbwf67gqkYgpPOPc509e7b+/e9/68Ybb1S/fv00Y8YMh48zadKkoj9HR0ercePG2rJli5o3b64+ffpcbTwAAAAA5cChBiIjI0Mffvih3n//feXk5OjBBx9Ubm6ulixZclVPYLqcsLAwhYWFOeVYAAAAgKOYRF02uxuIPn36aOPGjbr33nuVmJioXr16yd3dXbNmzXL4pJ9//rndn73vvvscPj4AAACA8mF3A7FixQo9//zzevrpp9W8efNrOqm9T2uy2WzKz8+/pnMBAAAAcB67J1Fv2rRJJ0+e1O2336477rhD06dPV2Zm5lWdtKCgwK4XzQMAAAAqmpvN3MsV2N1AdOzYUe+9954OHz6s2NhYzZs3TzfeeKMKCgr0xRdf6OTJk+WZEwAAAEAl4PBjXGvUqKHHHntMmzZtUmpqqoYOHapJkyapXr16Ds1XWLdunYKDg5WTk1NiX3Z2tkJCQrRx40ZH4wEAAADXxGazGXu5gmtaByIoKEhTpkzRL7/8ok8//dSh7yYmJmrw4MGqVatWiX0+Pj6KjY1VQkLCtcQDAAAA4GTXvJCcJLm7uysqKsqhpyvt2bNHvXr1KnV/eHi4du7c6Yx4AAAAgN2YA1E2pzQQV+PIkSPy9PQsdb+Hh4eOHTtWgYkAAAAAXImxBiIgIEB79+4tdX9KSooaNGhQgYkAAAAAXImxBiIiIkJjxozR2bNnS+w7c+aMxo0bp8jISAPJHDNv7hz1vudPat+2tR6KeUCpKSmmIzmV1euTrF8j9bk+q9Y4sGMj/XNQW619qbOWPxemyf1C1NjvOtOxyoVVr+EF1AersdnMvVyBsQZi9OjRysrK0i233KIpU6YoKSlJSUlJmjx5soKCgpSVlaVRo0aZimeXlSuW6/Up8Yp95lnNW/iZgoJa6OnYx3X8+HHT0ZzC6vVJ1q+R+lyflWts27i2Ficf0hMf79Lz81Pk4WbTm9Ft5O1p7D9N5cLK11CiPqAqshUWFhaaOvmBAwf09NNPa9WqVboQw2azqWfPnpoxY4aaNm16Vcc9e86ZKUv3UMwDCmnVWiNHj5V0foG88Lu7qf+Ah/X44CcrJkQ5snp9kvVrpD7XZ6rG7q9vKLdjl6b2dZ5a+UInPTVnt3b/nF3u51v/127lfg7J+v9Oqa/8eHuU6+FRhsT/phs794t3Xt3PvxXJ6K95mjRpouXLlyszM1Pbtm3T1q1blZmZqeXLl19181BR/sjL07fffK2OYZ2Ktrm5ualjx05K2bPLYDLnsHp9kvVrpD7XVxVqvFhNL3dJUs6ZPwwncR6rX0PqA6qmSjFO7Ovrq/bt26tDhw7y9fU1HccuJ347ofz8fNWpU6fY9jp16igzM9NQKuexen2S9WukPtdXFWq8wCbpxR6B2vNztvZnnjYdx2msfg2pD6iaGBwDABg3LLy5mt1QQ09+wm91AZjnKusxmEIDcZV8a/vK3d29xCSq48ePq27duoZSOY/V65OsXyP1ub6qUKMkDb0nUJ0D/fTUnD06djLPdBynsvo1pD6gaqoUtzC5Is9q1dQyOETbtm4p2lZQUKBt27aoza1tDSZzDqvXJ1m/RupzfVWhxqH3BKrbLXU15NMUHc4u+VhvV2f1a0h9sCoe41o2RiCuwcODHtWYkS8rJKSVWrVuo08+/khnzpxRVN9+pqM5hdXrk6xfI/W5PivXOCw8UOHB/hq+eK9O5Z2TXw1PSdKp3HzlniswnM55rHwNJeoDqiIaiGvQq3eETmRl6e3pbykz85iCWrTU2+/MVh2LDGtavT7J+jVSn+uzco1/vi1AkjTzodBi219d9p2WpR4xkKh8WPkaStQHVEVG14EoLxW1DgQAWJWJdSAqWkWtAwFcLdaBMGfG5p+MnfvZzjcZO7e9jM2BeOONN3TgwAFTpwcAAABwFYw1EMOGDVOzZs10zz33aP78+crLs9aTNwAAAOCamERdNqNPYZo9e7Zq1Kihhx9+WDfeeKNefPFF7d2712QkAAAAAGUw2kBERERoyZIl+uWXXzR8+HCtWrVKt956qzp06KD33ntPJ0+eNBkPAAAAVZCbzdzLFVSKdSDq1aun4cOH69tvv9X69esVHBysl156SQ0aNDAdDQAAAMBFjDUQtlJu8rrzzjv14Ycf6tChQ0pISKjgVAAAAADKYuwBYVd6emytWrU0ePDgCkoDAAAAnOfmKrOZDTHWQBQUWGeVUQAAAKCqYIkSAAAA4CIMQJStUkyiBgAAAOAaaCAAAAAA2I1bmAAAAICLMIm6bIxAAAAAALCb8QZi5cqV2rRpU9H7GTNmKDQ0VAMGDNCJEycMJgMAAEBVZLOZe7kC4w3EsGHDlJOTI0lKTU3V0KFDFRERofT0dMXFxRlOBwAAAOBixudApKenKzg4WJK0ePFiRUZGauLEiUpOTlZERIThdAAAAKhqjP+GvZIz/vdTrVo1nT59WpK0Zs0ahYeHS5L8/PyKRiYAAAAAVA7GRyC6dOmiuLg4de7cWdu3b9f8+fMlSWlpaWrYsKHhdAAAAAAuZnwEYvr06fLw8NCiRYs0c+ZMBQQESJJWrFihXr16GU4HAACAqsZmsxl7uQLjIxCNGzfW0qVLS2xPSEgwkAYAAABAWYyPQCQnJys1NbXofVJSkqKiojRy5Ejl5eUZTAYAAICqyGbw5QqMNxCxsbFKS0uTJO3fv18xMTGqXr26Fi5cqOHDh1/x+7m5ucrJySn2ys3NLe/YAAAAQJVkKywsLDQZwMfHR8nJyWrWrJkmT56sdevWadWqVdq8ebNiYmL0888/l/n98ePHa8KECcW2jRozTqPHji/H1AAAV9f99Q2mI5Sr9X/tZjoCrpG38RvNq65//a/snz/L08B2jYyd217G/2kWFhaqoKBA0vnHuEZGRkqSGjVqpMzMzCt+f8SIESUWnCt093J+UAAAAFQJbi4ymdkU4w1Eu3bt9Nprr6lHjx7asGGDZs6cKen8AnP+/v5X/L6Xl5e8vIo3DGfPlUtUAAAAoMozPgciMTFRycnJGjJkiEaNGqXAwEBJ0qJFi9SpUyfD6QAAAFDVMIm6bMZHINq0aVPsKUwXTJ06Ve7u7gYSAQAAACiN8QaiNN7e3qYjAAAAoApiCkTZjDcQ+fn5SkhI0IIFC3Tw4MESaz9kZWUZSgYAAADgUsbnQEyYMEHTpk1TdHS0srOzFRcXp379+snNzU3jx483HQ8AAADARYw3EHPmzNF7772noUOHysPDQ/3799fs2bM1duxYbd261XQ8AAAAVDE2m83YyxUYbyAyMjLUunVrSVLNmjWVnZ0tSYqMjNSyZctMRgMAAABwCeMNRMOGDXX48GFJUrNmzbR69WpJ0o4dO0qs7wAAAACUNzeDL1dgPGffvn21du1aSdJzzz2nMWPGqHnz5ho4cKAee+wxw+kAAAAAXMz4U5gmTZpU9Ofo6Gg1btxYW7ZsUfPmzdWnTx+DyQAAAABcyngDcamwsDCFhYWZjgEAAIAqylUmM5tipIH4/PPP7f7sfffdV45JAAAAADjCSAMRFRVl1+dsNpvy8/PLNwwAAABwEVcZf9i4caOmTp2qnTt36vDhw/rss8+u+HP2+vXrFRcXp6+//lqNGjXS6NGj9cgjjzh0XiOTqAsKCux60TwAAAAAl3fq1CndeuutmjFjhl2fT09P17333qu77rpLu3fv1osvvqgnnnhCq1atcui8lW4OBAAAAGCSq8yB6N27t3r37m3352fNmqWmTZvqjTfekCS1bNlSmzZtUkJCgnr27Gn3cYw9xnXdunUKDg5WTk5OiX3Z2dkKCQnRxo0bDSQDAAAAzMjNzVVOTk6xV25urlOOvWXLFvXo0aPYtp49e2rLli0OHcdYA5GYmKjBgwerVq1aJfb5+PgoNjZWCQkJBpIBAAAAZsTHx8vHx6fYKz4+3inHzsjIkL+/f7Ft/v7+ysnJ0ZkzZ+w+jrEGYs+ePerVq1ep+8PDw7Vz584KTAQAAACYXYl6xIgRys7OLvYaMWJEBVRtP2NzII4cOSJPT89S93t4eOjYsWMVmAgAAAAwy8vLS15eXuVy7Pr16+vIkSPFth05ckS1atXSddddZ/dxjI1ABAQEaO/evaXuT0lJUYMGDSowEQAAAHB+ErWpV3kKCwvT2rVri2374osvHF7E2VgDERERoTFjxujs2bMl9p05c0bjxo1TZGSkgWSOmTd3jnrf8ye1b9taD8U8oNSUFNORnMrq9UnWr5H6XJ/Va7RyfQM7NtI/B7XV2pc6a/lzYZrcL0SN/ez/LZ+rsPI1lKxfH1zX77//rt27d2v37t2Szj+mdffu3Tp48KCk87dDDRw4sOjzTz31lPbv36/hw4fru+++09tvv60FCxbopZdecui8xhqI0aNHKysrS7fccoumTJmipKQkJSUlafLkyQoKClJWVpZGjRplKp5dVq5YrtenxCv2mWc1b+FnCgpqoadjH9fx48dNR3MKq9cnWb9G6nN9Vq/R6vW1bVxbi5MP6YmPd+n5+SnycLPpzeg28vY09p9fp7P6NbR6fXBt//vf/9S2bVu1bdtWkhQXF6e2bdtq7NixkqTDhw8XNROS1LRpUy1btkxffPGFbr31Vr3xxhuaPXu2Q49wlSRbYWFhofPKcMyBAwf09NNPa9WqVboQw2azqWfPnpoxY4aaNm16Vcc9e86ZKUv3UMwDCmnVWiNHn79IBQUFCr+7m/oPeFiPD36yYkKUI6vXJ1m/RupzfVav0WR93V/fUK7Hv5za13lq5Qud9NSc3dr9c3a5nmv9X7uV6/Ev4N9o+fFmtS5jlqRkGDt3VJv6xs5tL6O/AmnSpImWL1+uzMxMbdu2TVu3blVmZqaWL19+1c1DRfkjL0/ffvO1OoZ1Ktrm5uamjh07KWXPLoPJnMPq9UnWr5H6XJ/Va7R6fZdT08tdkpRz5g/DSZzD6tfQ6vUBV6tSjKH6+vqqffv26tChg3x9fU3HscuJ304oPz9fderUKba9Tp06yszMNJTKeaxen2T9GqnP9Vm9RqvXdymbpBd7BGrPz9nan3nadBynsPo1tHp9KJ3NZu7lChgcAwCgAgwLb65mN9TQk5/wm2sAro0G4ir51vaVu7t7iUlUx48fV926dQ2lch6r1ydZv0bqc31Wr9Hq9V1s6D2B6hzop6fm7NGxk3mm4ziN1a+h1etD6dzkIkMBhlSKW5hckWe1amoZHKJtW7cUbSsoKNC2bVvU5ta2BpM5h9Xrk6xfI/W5PqvXaPX6Lhh6T6C63VJXQz5N0eHsko8ud2VWv4ZWrw+4WoxAXIOHBz2qMSNfVkhIK7Vq3UaffPyRzpw5o6i+/UxHcwqr1ydZv0bqc31Wr9Hq9Q0LD1R4sL+GL96rU3nn5FfDU5J0KjdfuecKDKdzDqtfQ6vXB1wNGohr0Kt3hE5kZent6W8pM/OYglq01NvvzFYdiwxrWr0+yfo1Up/rs3qNVq/vz7cFSJJmPhRabPury77TstQjBhI5n9WvodXrw+W5ymRmU4yuA1FeKmodCACA6zKxDkRFqqh1IFB+WAfCnKV7zTX4ka38jZ3bXkbnQCxdulRjx47V5s2bJUnr1q1TRESEevXqpXfffddkNAAAAFRRNoP/cwXGGoh33nlHffv21fLlyxUREaFPPvlEUVFRCggI0E033aQXX3xRb775pql4AAAAAC7D2ODYW2+9pbfffluDBw/Wl19+qYiICL3xxht65plnJEkdO3bUlClT9MILL5iKCAAAAOASxkYg0tPT1bNnT0nSXXfdpfz8fHXt2rVof/fu3XXgwAFT8QAAAFBFsRJ12Yw1EHXq1ClqEA4dOqRz587p4MGDRfsPHDggPz8/U/EAAAAAXIaxW5juv/9+Pf744xo0aJA+//xzDRw4UEOHDpWbm5tsNpuGDRum8PBwU/EAAABQRbESddmMNRCTJ09WXl6e5s2bp06dOukf//iH3nrrLd1///36448/1K1bN8XHx5uKBwAAAOAyKt06EGfPntUff/yh66+//uqPwToQAIArYB0IVHasA2HOqm+OGTt3z+AbjJ3bXpXun6a3t7e8vb1NxwAAAABwGUYXkgMAAADgWirdCAQAAABgkqs8TtUURiAAAAAA2I0RCAAAAOAiNh7jWibjIxArV67Upk2bit7PmDFDoaGhGjBggE6cOGEwGQAAAIBLGW8ghg0bppycHElSamqqhg4dqoiICKWnpysuLs5wOgAAAAAXM34LU3p6uoKDgyVJixcvVmRkpCZOnKjk5GRFREQYTgcAAICqxo07mMpkfASiWrVqOn36tCRpzZo1Cg8PlyT5+fkVjUwAAAAAqByMj0B06dJFcXFx6ty5s7Zv36758+dLktLS0tSwYUPD6QAAAFDVMIm6bMZHIKZPny4PDw8tWrRIM2fOVEBAgCRpxYoV6tWrl+F0AAAAAC5mfASicePGWrp0aYntCQkJBtIAAAAAKIvxEYjk5GSlpqYWvU9KSlJUVJRGjhypvLw8g8kAAABQFdls5l6uwHgDERsbq7S0NEnS/v37FRMTo+rVq2vhwoUaPnz4Fb+fm5urnJycYq/c3Nzyjg0AAABUSbbCwsJCkwF8fHyUnJysZs2aafLkyVq3bp1WrVqlzZs3KyYmRj///HOZ3x8/frwmTJhQbNuoMeM0euz4ckwNAEDl1v31DaYjlLv1f+1mOkK58jZ+o3nVtf77LGPn7h7kZ+zc9jL+T7OwsFAFBQWSzj/GNTIyUpLUqFEjZWZmXvH7I0aMKLHgXKG7l/ODAgAAADDfQLRr106vvfaaevTooQ0bNmjmzJmSzi8w5+/vf8Xve3l5ycureMNw9ly5RAUAAEAVwEJyZTM+ByIxMVHJyckaMmSIRo0apcDAQEnSokWL1KlTJ8PpAAAAAFzM+AhEmzZtij2F6YKpU6fK3d3dQCIAAAAApTHeQJTG29vbdAQAAABUQaxEXTbjDUR+fr4SEhK0YMECHTx4sMTaD1lZ5mbBAwAAACjO+ByICRMmaNq0aYqOjlZ2drbi4uLUr18/ubm5afz48abjAQAAoIphIbmyGW8g5syZo/fee09Dhw6Vh4eH+vfvr9mzZ2vs2LHaunWr6XgAAAAALmK8gcjIyFDr1q0lSTVr1lR2drYkKTIyUsuWLTMZDQAAAMAljDcQDRs21OHDhyVJzZo10+rVqyVJO3bsKLG+AwAAAFDebAZfrsB4A9G3b1+tXbtWkvTcc89pzJgxat68uQYOHKjHHnvMcDoAAAAAFzP+FKZJkyYV/Tk6OlqNGzfWli1b1Lx5c/Xp08dgMgAAAFRFbq4ym9kQ4w3EpcLCwhQWFmY6BgAAAIDLMNJAfP7553Z/9r777ivHJAAAAEBxjD+UzUgDERUVZdfnbDab8vPzyzcMAAAAALsZaSAKCgpMnBYAAADANap0cyAAAAAAo7iHqUzGHuO6bt06BQcHKycnp8S+7OxshYSEaOPGjQaSAQAAACiNsQYiMTFRgwcPVq1atUrs8/HxUWxsrBISEgwkAwAAQFVmM/g/V2CsgdizZ4969epV6v7w8HDt3LmzAhMBAAAAuBJjDcSRI0fk6elZ6n4PDw8dO3asAhMBAAAAuBJjDURAQID27t1b6v6UlBQ1aNCgAhMBAAAAks1m7uUKjDUQERERGjNmjM6ePVti35kzZzRu3DhFRkYaSOaYeXPnqPc9f1L7tq31UMwDSk1JMR3Jqaxen2T9GqnP9Vm9RqvXJ1m3xoEdG+mfg9pq7Uudtfy5ME3uF6LGfteZjuV0Vr1+wNUy1kCMHj1aWVlZuuWWWzRlyhQlJSUpKSlJkydPVlBQkLKysjRq1ChT8eyycsVyvT4lXrHPPKt5Cz9TUFALPR37uI4fP246mlNYvT7J+jVSn+uzeo1Wr0+ydo1tG9fW4uRDeuLjXXp+foo83Gx6M7qNvD2N/XjhdFa+fiidzeDLFdgKCwsLTZ38wIEDevrpp7Vq1SpdiGGz2dSzZ0/NmDFDTZs2varjnj3nzJSleyjmAYW0aq2Ro8dKOr9AXvjd3dR/wMN6fPCTFROiHFm9Psn6NVKf67N6jVavTzJXY/fXN5TbsUtT+zpPrXyhk56as1u7f84u9/Ot/2u3cj+HyX+j3qzWZcyO/eX/77c07W/2MXZuexn9FUGTJk20fPlyZWZmatu2bdq6dasyMzO1fPnyq24eKsofeXn69puv1TGsU9E2Nzc3dezYSSl7dhlM5hxWr0+yfo3U5/qsXqPV65OqRo0Xq+nlLknKOfOH4STOUdWuHy7CEESZKsUYo6+vr9q3b68OHTrI19fXdBy7nPjthPLz81WnTp1i2+vUqaPMzExDqZzH6vVJ1q+R+lyf1Wu0en1S1ajxApukF3sEas/P2dqfedp0HKeoStcPcASDYwAA4JoNC2+uZjfU0JOf8Jt5wOpoIK6Sb21fubu7l5hEdfz4cdWtW9dQKuexen2S9WukPtdn9RqtXp9UNWqUpKH3BKpzoJ+emrNHx07mmY7jNFXl+qEkV1kR2pRKcQuTK/KsVk0tg0O0beuWom0FBQXatm2L2tza1mAy57B6fZL1a6Q+12f1Gq1en1Q1ahx6T6C63VJXQz5N0eHsko9md2VV4foBV4MRiGvw8KBHNWbkywoJaaVWrdvok48/0pkzZxTVt5/paE5h9fok69dIfa7P6jVavT7J2jUOCw9UeLC/hi/eq1N55+RXw1OSdCo3X7nnCgyncw4rXz+UzlUWdDOFBuIa9OodoRNZWXp7+lvKzDymoBYt9fY7s1XHIsOaVq9Psn6N1Of6rF6j1euTrF3jn28LkCTNfCi02PZXl32nZalHDCRyPitfP+BqGV0HorxU1DoQAABUVibWgahoFbEOhEmsA2HOzp9yjJ379ptqGTu3vYz+0zxz5ow+/fRTbdq0SYcPH5abm5tuvvlmRUVF6e677zYZDQAAAFUUdzCVzdgk6n379qlly5YaMWKE1qxZo1WrVslms2nHjh3q2bOnHnzwQZ07x1ACAAAAUJkYayCef/559erVSxkZGTp48KDi4+NVUFCgrVu36ttvv9WOHTv02muvmYoHAACAqoqVqMtkbA5EjRo1tHv3bjVv3lySlJeXp5o1a+rw4cOqU6eOkpKS9OKLLyo9Pd3hYzMHAgBQ1TEHwvUxB8Kc5APm5kDc1oQ5EKWqXbu2Tp48WfT+9OnTOnfunKpVqyZJatOmjQ4fPmwqHgAAAKooFpIrm7FbmO655x7FxcXpu+++U3p6up566imFhobq+uuvlyQdPHhQ9erVMxUPAAAAwGUYG4GYMmWK7r//fgUHB8tms6lRo0b67LPPivYfO3ZMw4YNMxUPAAAAwGUYayDq1aunLVu26IcfflBubq5atGghD4//F+cvf/mLqWgAAACowliJumzGp+dcmEQNAAAAoPIz3kAAAAAAlQkDEGUzNokaAAAAgOuhgQAAAABgN25hAgAAAC7GPUxlMj4CsXLlSm3atKno/YwZMxQaGqoBAwboxIkTBpMBAAAAuJTxBmLYsGHKyTm/XHhqaqqGDh2qiIgIpaenKy4uznA6AAAAVDU2g/9zBcZvYUpPT1dwcLAkafHixYqMjNTEiROVnJysiIgIw+kAAAAAXMz4CES1atV0+vRpSdKaNWsUHh4uSfLz8ysamQAAAAAqis1m7uUKjI9AdOnSRXFxcercubO2b9+u+fPnS5LS0tLUsGFDw+kAAAAAXMz4CMT06dPl4eGhRYsWaebMmQoICJAkrVixQr169TKcDgAAAMDFjI9ANG7cWEuXLi2xPSEhwUAaAAAAVHUucieRMcZHIJKTk5Wamlr0PikpSVFRURo5cqTy8vIMJgMAAABwKeMNRGxsrNLS0iRJ+/fvV0xMjKpXr66FCxdq+PDhV/x+bm6ucnJyir1yc3PLOzYAAACsymbw5QJshYWFhSYD+Pj4KDk5Wc2aNdPkyZO1bt06rVq1Sps3b1ZMTIx+/vnnMr8/fvx4TZgwodi2UWPGafTY8eWYGgAAmNb99Q2mI5SrrX/rZjpClbX319+NnbtVQE1j57aX8TkQhYWFKigokHT+Ma6RkZGSpEaNGikzM/OK3x8xYkSJBecK3b2cHxQAAACA+QaiXbt2eu2119SjRw9t2LBBM2fOlHR+gTl/f/8rft/Ly0teXsUbhrPnyiUqAAAAqgBXWRHaFONzIBITE5WcnKwhQ4Zo1KhRCgwMlCQtWrRInTp1MpwOAAAAwMWMj0C0adOm2FOYLpg6darc3d0NJAIAAEBV5iorQptivIEojbe3t+kIAAAAAC5h/Bam/Px8vf766+rQoYPq168vPz+/Yi8AAACgIrnSU1xnzJihm266Sd7e3rrjjju0ffv2Uj/74YcfymazFXtdzS/tjTcQEyZM0LRp0xQdHa3s7GzFxcWpX79+cnNz0/jx403HAwAAACql+fPnKy4uTuPGjVNycrJuvfVW9ezZU0ePHi31O7Vq1dLhw4eLXgcOHHD4vMYbiDlz5ui9997T0KFD5eHhof79+2v27NkaO3astm7dajoeAAAAUClNmzZNgwcP1qOPPqrg4GDNmjVL1atX1z//+c9Sv2Oz2VS/fv2ilz1PPb2U8QYiIyNDrVu3liTVrFlT2dnZkqTIyEgtW7bMZDQAAABURQbvYcrNzVVOTk6xV25ubomIeXl52rlzp3r06FG0zc3NTT169NCWLVtKLe33339XkyZN1KhRI91///36+uuvHf7rMd5ANGzYUIcPH5YkNWvWTKtXr5Yk7dixo8T6DgAAAICVxcfHy8fHp9grPj6+xOcyMzOVn59fYgTB399fGRkZlz12UFCQ/vnPfyopKUmffPKJCgoK1KlTJ/3yyy8OZTT+FKa+fftq7dq1uuOOO/Tcc8/p//7v//T+++/r4MGDeumll0zHAwAAQBVjciG5ESNGKC4urtg2Z/1SPSwsTGFhYUXvO3XqpJYtW+qdd97Rq6++avdxjDcQkyZNKvpzdHS0GjdurC1btqh58+bq06ePwWQAAABAxfLy8rKrYahbt67c3d115MiRYtuPHDmi+vXr23UuT09PtW3bVvv27XMoo/FbmC4VFhamuLg4mgcAAACgFNWqVdPtt9+utWvXFm0rKCjQ2rVri40ylCU/P1+pqalq0KCBQ+c2MgLx+eef2/3Z++67rxyTAAAAAMW5ykrUcXFxGjRokNq1a6cOHTooMTFRp06d0qOPPipJGjhwoAICAormULzyyivq2LGjAgMD9dtvv2nq1Kk6cOCAnnjiCYfOa6SBiIqKsutzNptN+fn55RsGAAAAcEHR0dE6duyYxo4dq4yMDIWGhmrlypVFE6sPHjwoN7f/d8PRiRMnNHjwYGVkZMjX11e33367vvrqKwUHBzt0XlthYWGhUyupBM6eM50AAACUt+6vbzAdoVxt/Vs30xGqrLSM08bOfUv96sbOba9KNwcCAAAAQOVlrIFYt26dgoODlZOTU2Jfdna2QkJCtHHjRgPJAAAAAJTGWAORmJiowYMHq1atWiX2+fj4KDY2VgkJCQaSAQAAoEozuBK1KzDWQOzZs0e9evUqdX94eLh27txZgYkAAAAAXImxheSOHDkiT0/PUvd7eHjo2LFjFZgIAAAAMLsStSswNgIREBCgvXv3lro/JSXF4UUtAAAAAJQvYw1ERESExowZo7Nnz5bYd+bMGY0bN06RkZEGkjlm3tw56n3Pn9S+bWs9FPOAUlNSTEdyKqvXJ1m/RupzfVav0er1Sdav0ar1DezYSP8c1FZrX+qs5c+FaXK/EDX2u850LFQAm83cyxUYayBGjx6trKws3XLLLZoyZYqSkpKUlJSkyZMnKygoSFlZWRo1apSpeHZZuWK5Xp8Sr9hnntW8hZ8pKKiFno59XMePHzcdzSmsXp9k/Rqpz/VZvUar1ydZv0Yr19e2cW0tTj6kJz7epefnp8jDzaY3o9vI25On4KNqM7qQ3IEDB/T0009r1apVuhDDZrOpZ8+emjFjhpo2bXpVx62oheQeinlAIa1aa+TosZKkgoIChd/dTf0HPKzHBz9ZMSHKkdXrk6xfI/W5PqvXaPX6JOvXaLK+il5IrvZ1nlr5Qic9NWe3dv+cXe7nYyE5c/YdPWPs3IH1Kv8ol9EWukmTJlq+fLkyMzO1bds2bd26VZmZmVq+fPlVNw8V5Y+8PH37zdfqGNapaJubm5s6duyklD27DCZzDqvXJ1m/RupzfVav0er1Sdav0er1Xaqml7skKefMH4aToLzxFNeyVYoxOF9fX7Vv314dOnSQr6+v6Th2OfHbCeXn56tOnTrFttepU0eZmZmGUjmP1euTrF8j9bk+q9do9fok69do9fouZpP0Yo9A7fk5W/szT5uOAxhl7DGuAAAArmJYeHM1u6GGnvzEeiMruAxXGQowxHgDsX37dm3ZskUZGRmSpPr16yssLEwdOnQwnKxsvrV95e7uXmKS2PHjx1W3bl1DqZzH6vVJ1q+R+lyf1Wu0en2S9Wu0en0XDL0nUJ0D/fTUnD06djLPdBzAOGO3MB09elR33nmnOnbsqISEBK1bt07r1q1TQkKCOnbsqDvvvFNHjx41Fe+KPKtVU8vgEG3buqVoW0FBgbZt26I2t7Y1mMw5rF6fZP0aqc/1Wb1Gq9cnWb9Gq9cnnW8eut1SV0M+TdHh7JKPngeqImMjEM8884zy8/P17bffKigoqNi+77//Xo899pieffZZLVy40FDCK3t40KMaM/JlhYS0UqvWbfTJxx/pzJkziurbz3Q0p7B6fZL1a6Q+12f1Gq1en2T9Gq1c37DwQIUH+2v44r06lXdOfjU8JUmncvOVe67AcDqUJ1aiLpuxBmLVqlXauHFjieZBkoKCgvTWW2+pe/fuFR/MAb16R+hEVpbenv6WMjOPKahFS739zmzVsciwrdXrk6xfI/W5PqvXaPX6JOvXaOX6/nxbgCRp5kOhxba/uuw7LUs9YiARUDkYWweibt26Wrx4sbp1u/wzjtevX6+//OUvV/UUh4paBwIAAJhT0etAVDTWgTAnPdPc7WpN63obO7e9jM2BiI6O1qBBg/TZZ58pJyenaHtOTo4+++wzPfroo+rfv7+peAAAAAAuw9gtTNOmTVNBQYFiYmJ07tw5VatWTZKUl5cnDw8PPf7443r99ddNxQMAAEAVxQyIshm7hemCnJwc7dy5s9hjXG+//XbVqlXrqo/JLUwAAFgftzChvPxk8Bamm1zgFibj60DUqlVLd911l+kYAAAAAOxgbA7ElRw5ckSvvPKK6RgAAACoamwGXy6g0jYQGRkZmjBhgukYAAAAAC5i7BamlJSUMvd///33FZQEAAAA+H9YSK5sxhqI0NBQ2Ww2XW4O94XtNhsXDwAAAKhMjDUQfn5+mjJliu6+++7L7v/666/Vp0+fCk4FAAAAoCzGGojbb79dhw4dUpMmTS67/7fffrvs6AQAAABQnrgJpmzGGoinnnpKp06dKnV/48aN9cEHH1RgIgAAAABXYqyB6Nu3b5n7fX19NWjQoApKAwAAAJzHAETZKu1jXAEAAABUPsZXogYAAAAqE+ZAlI0RCAAAAAB2o4EAAAAAYDduYQIAAACK4R6mshgfgVi5cqU2bdpU9H7GjBkKDQ3VgAEDdOLECYPJAAAAAFzKeAMxbNgw5eTkSJJSU1M1dOhQRUREKD09XXFxcYbTAQAAoKqx2cy9XIHxW5jS09MVHBwsSVq8eLEiIyM1ceJEJScnKyIiwnA6AAAAABczPgJRrVo1nT59WpK0Zs0ahYeHS5L8/PyKRiYAAAAAVA7GRyC6dOmiuLg4de7cWdu3b9f8+fMlSWlpaWrYsOEVv5+bm6vc3Nxi2wrdveTl5VUueQEAAGBtLnInkTG2wsLCQpMBDh48qGeeeUY///yznn/+eT3++OOSpJdeekn5+fl66623yvz++PHjNWHChGLbxo0bp/Hjx5dXZAAAAFjYod/yjJ37xtrVjJ3bXsYbiGt1uREILy9GIAAAAHB1DmebayAa+FT+BsL4HIjk5GSlpqYWvU9KSlJUVJRGjhypvLwrXzwvLy/VqlWr2IvmAQAAACgfxhuI2NhYpaWlSZL279+vmJgYVa9eXQsXLtTw4cMNpwMAAEBVYzP4P1dgvIFIS0tTaGioJGnhwoXq2rWr5s6dqw8//FCLFy82Gw4AAABAMcYbiMLCQhUUFEg6/xjXC2s/NGrUSJmZmSajAQAAALiE8ce4tmvXTq+99pp69OihDRs2aObMmZLOLzDn7+9vOB0AAACqHNe4k8gY4yMQiYmJSk5O1pAhQzRq1CgFBgZKkhYtWqROnToZTgcAAADgYpX2Ma5nz56Vu7u7PD09TUcBAABAFXIk5w9j5/avVfl/9jV+C1NpvL29TUcAAAAAcAnjDUR+fr4SEhK0YMECHTx4sMTaD1lZWYaSAQAAALiU8TkQEyZM0LRp0xQdHa3s7GzFxcWpX79+cnNz0/jx403HAwAAQBVjs5l7uQLjcyCaNWumt956S/fee6+uv/567d69u2jb1q1bNXfuXJPxAAAAUMUcPWluDkS96yv/HAjjIxAZGRlq3bq1JKlmzZrKzs6WJEVGRmrZsmUmowEAAKAKYiXqshlvIBo2bKjDhw9LOj8asXr1aknSjh075OXlZTIaAAAAgEsYbyD69u2rtWvXSpKee+45jRkzRs2bN9fAgQP12GOPGU4HAACAKsdm8OUCjM+BuNSWLVu0ZcsWNW/eXH369DEdBwAAAFXMsd/PGTv3DTWNPyT1iipdAwEAAACYRANRNiMJP//8c7s/e99995VjEgAAAKA4F7mTyBgjIxBubvZNvbDZbMrPzy/nNAAAAMD/k2lwBKIuIxCXV1BQYOK0AAAAwBW5yoJuphh/ChMAAAAA12GsgVi3bp2Cg4OVk5NTYl92drZCQkK0ceNGA8kAAAAAlMZYA5GYmKjBgwerVq1aJfb5+PgoNjZWCQkJBpIBAACgKmMl6rIZayD27NmjXr16lbo/PDxcO3furMBEAAAAAK7E2DTvI0eOyNPTs9T9Hh4eOnbsWAUmAgAAAJhEfSXGRiACAgK0d+/eUvenpKSoQYMGFZgIAAAAwJUYayAiIiI0ZswYnT17tsS+M2fOaNy4cYqMjDSQDAAAAEBpjCwkJ52/hem2226Tu7u7hgwZoqCgIEnSd999pxkzZig/P1/Jycny9/c3EQ8AAABV1InT5hYy9q3ubuzc9jLWQEjSgQMH9PTTT2vVqlW6EMNms6lnz56aOHGiUlJSNHDgQFPxAAAAUAXRQJTNaANxwYkTJ7Rv3z4VFhaqefPm8vX11Z49e3TbbbcpP9/cBQQAAEDV89sZcz9/1r6u8jcQxp7CdPECcu7u7kW3MF3Yd/LkSROxAAAAAJTBWANRu3Zt2cp4RlZhYWGZ+wEAAIDy4CoLuplirIG4/vrrNWrUKN1xxx2X3f/DDz8oNja2glMBAAAAKIuxBuK2226TJHXr1u2y+2vXrq1KMD0DAAAAwEWMNRADBgzQmTNnSt1fv359jRs3rgITAQAAAKxEfSWV4ilMAAAAQGWRc7bA2LlreRtb59luxkYgAAAAgMqIAYiyVdoW58iRI3rllVdMxwAAAABwkUp7CxMLyQEAAMCEkwZvYbqeW5hKl5KSUub+77//voKSAAAAABfhHqYyGRuBcHNzk81mu+yjWi9st9lsjEAAAACgQp3MNTgC4cUIRKn8/Pw0ZcoU3X333Zfd//XXX6tPnz4VnAoAAABVHStRl81YA3H77bfr0KFDatKkyWX3//bbbywkBwAAAFQyxhqIp556SqdOnSp1f+PGjfXBBx9UYCIAAACAheSupNI+hQkAAAAw4VSeuR+Pa1Sr/N1L5Z+lAQAAAKDSYCVqAAAA4CKVfwzALEYgAAAAANiNEQgAAADgYgxBlIkRCAAAAAB2M95ArFy5Ups2bSp6P2PGDIWGhmrAgAE6ceKEwWQAAAAALmW8gRg2bJhycnIkSampqRo6dKgiIiKUnp6uuLg4w+kAAABQ1dgM/s9RM2bM0E033SRvb2/dcccd2r59e5mfX7hwoVq0aCFvb2+1bt1ay5cvd/icxhuI9PR0BQcHS5IWL16syMhITZw4UTNmzNCKFSsMpwMAAAAqp/nz5ysuLk7jxo1TcnKybr31VvXs2VNHjx697Oe/+uor9e/fX48//rh27dqlqKgoRUVFae/evQ6d1/hCcn5+ftq0aZOCg4PVpUsXDRw4UE8++aR++uknBQcH6/Tp0ybjAQAAoIo5e87cub0deMTRHXfcofbt22v69OmSpIKCAjVq1EjPPfec/va3v5X4fHR0tE6dOqWlS5cWbevYsaNCQ0M1a9Ysu89r/ClMXbp0UVzc/9fefYdFca9vA793RYoUFTudCCIosR4SBGyxALEETTTGxBJMxG7Un4pGgWMUEzWxRc2xRI167GLBgnoUTCyICoqg2ABFQERB6WWf9w/f3dCMSx1meD7XxXW5M8POczvL7Dw7M9+dAScnJ4SGhmLPnj0AgJiYGJiYmLzz93Nzc5Gbm1tsmpaWFrS0tKqlXsYYY4wxxqqLuse2eXl5uHbtGry9vVXT5HI5+vTpg0uXLpX53JcuXSp1i0D//v0REBBQrhoFv4Rp7dq10NDQwP79+7F+/XoYGxsDAE6cOAFXV9d3/r6/vz8aNmxY7Kd///6l/uOlJDc3F76+vpLNyPnET+oZOZ/4ST2j1PMB0s/I+YSlrSHcT1nHtv7+/qVqfP78OQoLC9GiRYti01u0aIGkpKQycyUlJZVr+bcR/BKmyirZpb169QqmpqZIT0+HgYGBgJVVn1evXqFhw4aSzcj5xE/qGTmf+Ek9o9TzAdLPyPnqLnXPQDx9+hTGxsa4ePEiHB0dVdNnz56N4OBgXLlypdRza2pqYtu2bRgxYoRq2rp16+Dn54fk5GS1axT8Eqbr16+jfv36sLe3BwAcPnwYv//+O+zs7ODr6wtNTc1//H2+XIkxxhhjjEmFuse2TZs2Rb169Uod+CcnJ6Nly5Zl/k7Lli3LtfzbCH4J0/jx4xETEwMAePjwIT7//HM0aNAA+/btw+zZswWujjHGGGOMsdpHU1MTXbp0wdmzZ1XTFAoFzp49W+yMRFGOjo7FlgeA06dPv3X5txG8gYiJiUHHjh0BvBmXtnv37ti1axe2bt2KAwcOCFscY4wxxhhjtdSMGTOwceNGbNu2DdHR0ZgwYQIyMzMxduxYAMCoUaOK3WQ9bdo0nDx5EitWrMCdO3fg6+uLsLAwTJ48uVzrFfwSJiKCQqEAAJw5cwYDBgwAAJiamuL58+flfj4tLS34+PhI+rImqWfkfOIn9YycT/yknlHq+QDpZ+R8TB3Dhw9HSkoKFi5ciKSkJHTs2BEnT55U3SgdHx8Pufzv8wXdunXDrl278P3332PevHmwtrZGQEAA2rdvX671Cn4Tde/evWFqaoo+ffrA09MTUVFRsLKyQnBwMEaPHo3Y2Fghy2OMMcYYY4wVIfglTCtXrsT169cxefJkzJ8/H1ZWVgCA/fv3o1u3bgJXxxhjjDHGGCtK8DMQb5OTk4N69eqhfv36QpfCGGOMMcYY+/9qbQPBGGOMMcYYq30Ev4m6sLAQv/zyC/bu3Yv4+Hjk5eUVm//ixQuBKmOMMcYYY4yVJPg9EH5+fvj5558xfPhwpKenY8aMGRgyZAjkcjl8fX2FLo8xVo2kfgJUOcKclEk9ozLf69evBa6kekj9b5AxVj0EbyB27tyJjRs3YubMmdDQ0MCIESOwadMmLFy4EJcvX6629Ur9TU/qbwpS337A3xkVCgUKCgoErqbqKRQKyGQyPH/+HAkJCUKXU+UUCgXkcjnu3buH8+fPC11OtSiacceOHcjJyRG6pCqlzHf37l18/fXXePjwodAlVSnl32BGRgaePXuGzMxMoUuqcnXhvaKoku/9Uj8WYMIRvIFISkqCvb09AEBPTw/p6ekAgAEDBiAwMLBa1ln0TWHq1KkYMWIE/Pz8EBkZWS3rq2nKN4WkpCQ8e/ZM6HKqnHL7PXz4EEuWLMHEiRPx22+/CV1WlSr6Gp00aRLc3d0xY8YMJCUlCV1alZHL5YiNjUWnTp3wyy+/SGrIZuX2Cw8PR+fOnREdHS10SVVOmfHmzZvo1q0bzp8/X6Hv7qmtlPkiIiLQuXNnHDhwAIcPHxa6rCqjzHf79m0MHToULi4u+OSTT7B//36hS6syyox37tzB9OnTMXz4cCxYsKBaP5wUkvK9PzMzE/n5+Xj9+jVkMpnQZVWZutYM1naCNxAmJiZITEwEALRu3RpBQUEAgKtXr1bbl4vI5XJER0fDwcEBsbGx0NLSwrp16zBx4kSsXr26WtZZU4hIlc/U1BQjR46U5Jv6rVu34OLiggsXLiAmJgZTpkzB3LlzhS6vSpTMmJaWho4dO2LTpk1YunSp0OVVqQsXLiAhIQFBQUHYuHEj4uPjhS6p0ooeeDo7O+Pbb7/FhAkThC6rysnlcjx58gSDBw/GmDFjsGnTJpiYmEjiE8+i29DR0RFTpkyBj48P9uzZI4nXKPD3+6CLiwvatm0LX19fEBG2b98uiW0IvMkYFRUFR0dHJCcnw9zcHHv37oWnpyeWL18udHlVSvmajYyMhJubGxwdHdGhQwesXbtWEh/OFG0G9+3bJ3Q5DABIYHPmzKHFixcTEdHu3btJQ0ODrKysSFNTk+bMmVPl61MoFJSfn0+enp40duxY1fSkpCQaM2YMOTg40JIlS6p8vTUpOTmZXFxcyM3NjUxNTalfv36UkpIidFlVJi4ujtq0aUOzZs1STTtw4AA1a9aMbt68KWBlVefRo0f03nvv0dy5c1XTVq5cSePGjaOcnBwBK6tad+/epTFjxtCaNWuoZcuWNHfuXEpOTha6rEqLiYkhbW1t1fbLy8ujffv20erVq+mPP/6gly9fCltgFTlz5gy5uroS0ZuMs2bNoo8//piGDRtG69evF7i6ygkLCyMDAwOaN28eEREdPXqU9PX16cSJE0REVFhYKGR5lZadnU3Dhg2jyZMnq6YFBwfTwIED6cmTJ5J4jWZmZtKAAQNo5syZqmmvXr0iIyMjMjQ0JG9vbwGrq3qPHj0iQ0NDmjp1Km3ZsoV8fHyocePG9OWXX9KFCxeELq/CFAoFEb3Zr+rq6pJMJqPffvtN4KqY4KMwFf1Edfjw4TAzM8OlS5dgbW2NgQMHVvn6ZDIZNDQ08Pz5c+jq6gJ486l9ixYt8NNPP8HHxwfHjh1D69atMWzYsCpff02IjIyEpaUlJkyYgIYNG6Jfv34YOXIkdu7ciaZNmwpdXqUQEQ4ePAhTU1N4e3urpr///vvQ0tKSxL0CCoUCR48eRf/+/TFnzhzV9Dt37iAsLAwffvgh2rdvj4EDB4r2NVrUuXPnsGHDBigUCixbtgx6enoIDQ2FmZkZ1qxZI3R55aZQKLB582bo6emhTZs2AIDBgwcjMTERWVlZiI2NRe/evbFw4UI4OjoKXG3lhIeH4+nTpwDeXHaan58PJycnREREYOPGjYiKihLdWV0iQm5uLtzc3PD1119j8eLFAN7k69u3L3x8fODi4qJ6/xArbW1tvHjxAhYWFqppx48fR1hYGD744ANYWFigY8eOWLt2rXBFVpKGhgZSU1NhZ2cHAMjMzIS+vj4++ugjJCYm4ty5czh48CCGDBkicKVV48SJE2jTpg1WrVqlmubg4IAFCxZg9erV0NbWRteuXQWssGJkMhnS09Ph4+MDNzc32NjYwMvLC4WFhZI8uysaAjcwNU6hUFBBQQFNmjSJ3Nzc6PXr16ppRESJiYnUu3dvcnd3F7jSiktLS6OQkBDV49u3b5OJiQn169ePnj17ppou1k/Qrl27RvPnzy813draWvXpoNg9e/aMrly5onq8ePFiksvl5OfnR7///jv16NGDunXrRvHx8QJWWTnKT5Xc3Nzo9u3bRES0adMm0tXVJQMDAzp27JiQ5VXKkydPaOrUqeTg4EBGRkbk7u5O9+7do9zcXLp//z5ZWVnRJ598InSZlfbnn39S9+7dafPmzdS3b196/PgxEb35dPunn34iBwcHun79usBVVkxiYqLq38p95Y4dO6h169b0119/FZsuNgqFgjIzM8nDw4NcXV1p6dKlNHv2bNLR0aHt27dTSEgILVu2jLp06UL//e9/hS63wtLS0sjS0lJ1FomIKCEhgWxsbGjnzp304Ycf0rBhwwSssGqtW7eO7Ozs6OXLl6RQKFT72FOnTlHbtm1p6tSplJ+fr5ouJvHx8TRr1iw6fPgwZWdn09KlS0kmk9G6deuIiESZSewEaSAOHz6s9k91uXHjBmlqapKvr69qmrKJCA8PJ5lMRlevXq229dcU5RtcVFSUqolISUmh/Px8WrVqVbX+H9eEojsNGxsbOnTokOpxYGAgJSQkCFBV5ZTcEb548YLmz59PJ0+eVE17/PgxyWQy2r9/f02XV+Xc3d1VbwLjxo2jhg0bUvPmzcnHx4cePXokbHEVoNx+CQkJNH78eBowYICqQVIKDg4mmUxG4eHhQpRYZeLj48nCwoLee+89cnJyovz8fNW8lJQUatiwIf3nP/8RsMKKedvBSEFBAdnY2NDIkSNruKLqERUVRR4eHjRmzBiyt7cvdtlZWloaWVlZ0ffffy9ghRWn3IY7duwgmUxGn376Kc2cOZP09fVp3LhxREQUFBRExsbG9PTpU9E2g0UdP36c6tevT+fOnSOiN5cVKu3evZvkcjldvHhRoOoqLzY2VvXvjIwM8vf3J5lMRr/++qtqen5+PqWmpgpRXp0jSAMhk8nU+pHL5dWyfuWOYtOmTSSXy+nHH38sNj8yMpJsbW3pzp071bJ+oSibCFdXVxo9ejTVq1ePYmJihC6r0vLz8ykvL49sbW3pzJkzREQ0b948kslkov6Evqjs7GzVvwsKCig6Opq6du1KYWFhAlZVOcqGfeHChbR27VqaMmUKtWrVimJjY2ndunWkra1NixYtKnZQKhbKg5fk5GQKCgoq9kZORHTs2DGysbER9f0eyox//vknGRgYkIGBgervj+jN9u3fv7/oP6RQUr5et23bRpaWlqK+ppzo7/dB5b6lZ8+etG3btmLz3d3d6eeffyYicX/CGxAQQN27dycPDw9avny5avqmTZvI3t6+1N9nbVdyWxR9PHbsWGrSpInqvT03N1c1r3379qWOd2qjf8pXVFZWFi1ZsqTYmYhp06bRwoULRbdNxUiQeyCEHopLLn8z+NSoUaOQkZGBmTNn4vHjxxg5ciTMzc2xa9cu5OTkoFGjRoLWWdVsbW0RGBiIjh07onHjxggNDYW1tbXQZVWacpg6IoKWlhYWL16MVatWITQ0FKampgJXVzWKjkhWr1497Ny5E8CbUczEql69egDe3L/y2WefoWXLljh69CjMzc0xYcIEyOVy9O7dGxoagt+qVW4ymQxEhObNm6Nv376l5oeEhMDExKTaRpqrCTKZDAqFAk5OTti9ezeGDh0KX19fxMXFwcHBATt27EBERAQ6dOggdKlVQvl67dq1KxQKBUJCQuDs7CxwVRWnfB/U0tJCTk4OiAhXr16Fm5sbNDQ0sHLlSly7dk11D4uYhwMdPHgw3N3dVfdAKt26dQuWlpbIz89H/fr1BaxQfcrRiJKSkiCXy9G8eXPV/kYmk2HevHl4+vQpXFxccPbsWbRr1w4AUFBQAB0dnVp/XPO2fGXR0dHBd999B5lMhunTp+OPP/7A5cuXcf36ddFsT1ETtn+pHY4cOULm5uZkYmJC1tbWZGZmRteuXRO6rCqXl5dHXl5epKurW+qSCilwcHCgdu3akaampiQuPyvLjRs3aP78+WRgYEARERFCl1Ml4uLiaNGiRarLeaRwKcHb3Lhxg+bNm0f6+vqSGTFMKSwsjHr16qXaj7Zr10609z+8y/Tp08nCwoJycnJE/cl8UadPnyZNTU2ysLCgzp07k6WlpWS3X2RkJE2bNo309fVFtR9VvtaioqJIQ0OD+vTpU+YIizdv3qRBgwaRlpYWLV++nDZt2kSzZ8+mxo0b0/3792u6bLWpm6+ktLQ06tSpExkaGopqe4qdYA3E2bNnydbWltLT00vNS0tLIzs7OwoODq7UOopei/yua+KSkpLoypUrFBwcTE+fPq3UemtKefIRvTl4cXBwoNDQ0OourUqom6+wsJDS09OpVatWJJfL6datWzVVYqWVZxs+ffqUhg8fTvb29qK5dl7dfGIdmrY82y8hIYEGDRpEbdq0Ec32I1Ivo7LpS09Pp7i4OIqOjhbNdcjl2YbKZSMiIopdj12blWf7RURE0IoVK2j79u2iyUdUvm2YmZlJGzZsIFdXV1H9HSqpO0z769evadGiRdShQwdq164dOTs7i6IhLO8w9IWFhTRr1iySyWSS+1CmthOsgRg4cKDq2sqyrFq1qlKjlCh3KAEBAeTk5ER79uyhV69e/eOyYlKefEpZWVmiGdu7IvlOnDhBUVFRNVFelahIxocPH4rmxvCK5BOTiuS7d++eaqQiMeD9qLiVJ59Yz/xVZBu+evWK0tLSaqK8Knf27FkaNWoUXbp0qdTgKGVJSkqizMzMMj+srY3Kmy8xMZG+++47UTaDYidYA2FmZvaPB3vR0dFkampaqXUEBASQrq4uLV26tMybacX4hlcU5xN3PiLpZ+R8ijL/LSa8DTlfbVcXMiqpO0y7GAefIKrYMPRiPYMtdoI1EFpaWnTv3r23zr937x5pa2tX+PmfPHlC7du3p1WrVhHRm+v/X79+TWfOnBHFabx34XziJ/WMnE/8pJ6R84lfXcj4NlIfpv1d+QICAgSusG4TbHgTY2NjREZGwsrKqsz5N2/eRKtWrSr8/BoaGtDV1YWJiQlSU1Oxbt06nDlzBpGRkWjatCmWLFmCoUOHVvj5hcb5xJ0PkH5GzifufID0M3I+cecD6kbGt1GOpGVra4ugoCD069cPX331FVq0aIEdO3YgOjpa4AorR+r5xE4u1Ird3d2xYMEC5OTklJqXnZ0NHx8fDBgwQO3nIyIAwLNnz5CVlQVtbW0QEdasWQNLS0vcuHEDQ4cOxenTp2FkZIRbt25VWZaawPnEnQ+QfkbOJ+58gPQzcj5x5wPqRsaKUA7TfurUKRw9elQyw7QrST2fKNXsCY+/JSUlkZGREZmamtKPP/5IAQEBFBAQQEuXLiVTU1MyMjKipKQktZ5LeX3jkSNHyNnZmY4cOUJERA8ePKD169fThg0bit1ANGDAAPLz86v6UNWE84k7H5H0M3I+cecjkn5GzifufER1I2NFSX2YdqnnEyNBvwciNjaW3NzcSC6XF/v2aTc3N3r48GG5nuvQoUOkp6dHixcvpgcPHpS5TGZmJs2dO5eaNm1Kd+/erYoINYbziTsfkfQzcj5x5yOSfkbOJ+58RHUjIxEP016S2PLVBbXii+RevHhBoaGhdOXKFXrx4kW5f//x48fUtm1bWrlyJRG9GX0gJyeHQkJCVCM97dixgzw8PMjc3Fx0N1ZxPnHnI5J+Rs4n7nxE0s/I+cSdj6huZCTiYdrLIqZ8dUWtaCAq69GjR9SlSxcKCQmh5ORk8vf3px49epCenh45OTnR//73P0pKSqIffvihVn8L49twPnHnI5J+Rs4n7nxE0s/I+cSdj6huZFSS+tC0Us9XF0iigXj58iWZmZmRk5MTNWnShDw8PGj58uV08eJF6tChAy1dupSIxPtFOZxP3PmIpJ+R84k7H5H0M3I+cecjqhsZiaQ/NK3U89UVgg3jWlFEBJlMhlevXqFBgwbIzs5Go0aNcPHiRWzduhVffPEFPv/8czRu3BgymQwmJiZQKBQAAJlMJnD178b5xJ0PkH5GzifufID0M3I+cecD6kbGt5H60LRSz1dnCNW5VITylNbx48dp4MCB9K9//YtGjRql+tbCot+8mJubS97e3tSsWTOKiYkRpN7y4nzizkck/YycT9z5iKSfkfOJOx9R3chYlDJvcnIyZWZmUlpaGjk4OFDPnj1JX1+fPDw8aNWqVXTt2jXq2bMn+fj4CFtwOUk9X10ligai6LVwAQEB1KBBA/Lz86O1a9fSZ599RkZGRhQcHKxadtu2beTu7k5mZmaiOB3G+cSdj0j6GTmfuPMRST8j5xN3PqK6kbEkqQ9NK/V8dVmtbiBSUlKKPb5z5w517tyZ1q9fT0RvvkvC2NiY3nvvPWrcuDGdP3+eiIji4uJo/vz5tf7TCM4n7nxE0s/I+cSdj0j6GTmfuPMR1Y2M/0TqQ9NKPV9dVWsbiNWrV5O9vT1FRkaqpkVHR5OXlxe9fv2a4uPjydramr755hsKDw+nTp06UYsWLSgoKIiIav9NVJxP3PmIpJ+R84k7H5H0M3I+cecjqhsZ/4nUh6aVer66rNY2EE+fPqXmzZtTr169in3rYEJCAhEReXl50WeffUZZWVlERPTFF1+Qvr4+WVpaUkZGRq0fAozziTsfkfQzcj5x5yOSfkbOJ+58RHUj4z+R+tC0Us9Xl8mFvom7KCICABQWFqJVq1aIiIjAnTt34OXlhcjISACAkZERsrOzERERATs7O+jo6AAADAwMsGbNGoSGhkJXV7dWjsLA+cSdD5B+Rs4n7nyA9DNyPnHnA+pGRnU1atQIKSkp8Pb2hp2dHUJDQzFw4EAEBQUhIyMDoaGhaNGiBby9vdG6dWuhyy03qeer04TqXEpSnoZ89uwZXb16lS5dukREf1/76OLiUuzTCU9PT7K1taU9e/bQ9OnTydTUlGJjYwWpXR2cT9z5iKSfkfOJOx+R9DNyPnHnI6obGd9GebYkPT2d8vPzVd++/OTJE/rhhx/o119/pdTUVNVyH3/8MS1ZsqTY79ZmUs/HiqsVDYRyh3L79m1ycnIiV1dXGjJkCGVnZxNR8R2L8jrJS5cukYeHB5mYmND7779fq6+b43zizkck/YycT9z5iKSfkfOJOx9R3cj4NlIfmlbq+VhpgjcQyhddZGQkNWrUiObNm0dxcXGqHY3yRafcsTg7O6vu0M/Ly6O4uDhKTU0Vpng1cD5x5yOSfkbOJ+58RNLPyPnEnY+obmQsi9SHppV6PvZ2gjcQRESpqank7OxMU6dOLTZd+cIsuWPp0aMH3bx5s8brrCjOJ+58RNLPyPnEnY9I+hk5n7jzEdWNjEpSH5pW6vnYu9WKBuL27dvUunVrCg4OLnNINoVCodrBJCYmkpaWFrm7u1Nubm5Nl1ohnE/c+Yikn5HziTsfkfQzcj5x5yOqGxmJpD80rdTzMfXUigZi586dpKGhodpxlPXiyszMpIsXLxLRm69DF1P3yvnEnY9I+hk5n7jzEUk/I+cTdz6iupGRSPpD00o9H1NPrRjG1cLCAhoaGjh48CAAQC4vXdaWLVvg4+ODrKwsNG/eHNbW1jVdZoVxPnHnA6SfkfOJOx8g/YycT9z5AGlnJIkPTSv1fKz8akUDYW5uDgMDA2zfvh1xcXGq6coXLADExsaiS5cuqhekmHA+cecDpJ+R84k7HyD9jJxP3PkA6WZUKBSQyWRISUnBjRs3cPnyZbRs2RI3btzAw4cPMXHiRERFRQEAdHR0YGdnh71792Lv3r347rvvEBgYiJ49e6Jp06YCJymb1POxChLmxEdpBw4cIC0tLfrqq6+KnRLLzMwkb29vMjc3V43IIEacT9z5iKSfkfOJOx+R9DNyPnHnI5JeRqkPTSv1fKziZERFWn8BKRQKbNy4EZMnT4aVlRUcHR2hra2NhIQEXL58GSdPnkSnTp2ELrPCOJ+48wHSz8j5xJ0PkH5GzifufIC0MhIRZDIZbt++DWdnZ0ycOBHjx4+HiYkJ5HI5CgoKoKGhgeTkZHTp0gWWlpbYvHkz2rRpg/z8fCQmJkJPTw+GhoZCRymT1POxyqk1DYRSaGgoli1bhvv370NfXx/dunWDp6enaK6DfBfOJ35Sz8j5xE/qGTmf+Ekl44sXLzB48GB07twZq1atUk1XHnyXPMi2srLCmjVrYG9vL2DV6pN6PlZxta6BAN7cpFOvXj2hy6g2nE/8pJ6R84mf1DNyPvGTQsaoqCgMGjQIW7ZsgbOzc6kbw5WHWDKZDElJSbCwsMBHH32EQ4cOQVNTU4iSy0Xq+VjFaQhdQFmKvkCVXa6UcD7xk3pGzid+Us/I+cRPChnDw8MRFxcHFxcXyGQyKBSKYrlkMhmysrIQEREBR0dHxMfHIz09XTQH11LPxyquVozCVFLRnYgYdyjvwvnET+oZOZ/4ST0j5xM/KWSU8tC0gPTzsYqrlQ0EY4wxxlhtJ9WhaZWkno9VHDcQjDHGGGMVYGxsjPXr1+PUqVNYsGCB6vsQlJf2zJs3D/v378fYsWNFeZZF6vlYxdXKm6gZY4wxxsRASkPTlkXq+VjFcAPBGGOMMVZJUhma9m2kno+VDzcQjDHGGGNVQApD0/4Tqedj6uN7IBhjjDHGqkDJoWmlRur5mPr4DARjjDHGGGNMbXwGgjHGGGOMMaY2biAYY4wxxhhjauMGgjHGGGOMMaY2biAYY4wxxhhjauMGgjHGGGOMMaY2biAYY4wxxhhjauMGgjHGymnMmDH45JNPVI979uyJ6dOn13gd58+fh0wmQ1paWrWto2TWiqiJOhljjNUcbiAYY5IwZswYyGQyyGQyaGpqwsrKCv/+979RUFBQ7es+ePAgFi1apNayNX0wbWFhgZUrV9bIuhhjjNUNGkIXwBhjVcXV1RW///47cnNzcfz4cUyaNAn169eHt7d3qWXz8vKgqalZJes1NDSskudhjDHGxIDPQDDGJENLSwstW7aEubk5JkyYgD59+uDIkSMA/r4UZ/HixTAyMoKNjQ0A4PHjxxg2bBgaNWoEQ0NDDB48GLGxsarnLCwsxIwZM9CoUSM0adIEs2fPBhEVW2/JS5hyc3MxZ84cmJqaQktLC1ZWVti8eTNiY2PRq1cvAEDjxo0hk8kwZswYAIBCoYC/vz8sLS2ho6ODDh06YP/+/cXWc/z4cbRp0wY6Ojro1atXsTororCwEJ6enqp12tjYYNWqVWUu6+fnh2bNmsHAwABeXl7Iy8tTzVOn9qLi4uIwcOBANG7cGLq6umjXrh2OHz9eqSyMMcZqDp+BYIxJlo6ODlJTU1WPz549CwMDA5w+fRoAkJ+fj/79+8PR0REXLlyAhoYGfvjhB7i6uuLmzZvQ1NTEihUrsHXrVmzZsgW2trZYsWIFDh06hN69e791vaNGjcKlS5ewevVqdOjQAY8ePcLz589hamqKAwcOYOjQobh79y4MDAygo6MDAPD398eOHTuwYcMGWFtbIyQkBF9++SWaNWuGHj164PHjxxgyZAgmTZqEb7/9FmFhYZg5c2al/n8UCgVMTEywb98+NGnSBBcvXsS3336LVq1aYdiwYcX+37S1tXH+/HnExsZi7NixaNKkCRYvXqxW7SVNmjQJeXl5CAkJga6uLqKioqCnp1epLIwxxmoQMcaYBIwePZoGDx5MREQKhYJOnz5NWlpaNGvWLNX8Fi1aUG5urup3/vjjD7KxsSGFQqGalpubSzo6OnTq1CkiImrVqhX99NNPqvn5+flkYmKiWhcRUY8ePWjatGlERHT37l0CQKdPny6zznPnzhEAevnypWpaTk4ONWjQgC5evFhsWU9PTxoxYgQREXl7e5OdnV2x+XPmzCn1XCWZm5vTL7/88tb5JU2aNImGDh2qejx69GgyNDSkzMxM1bT169eTnp4eFRYWqlV7ycz29vbk6+urdk2MMcZqFz4DwRiTjGPHjkFPTw/5+flQKBT44osv4Ovrq5pvb29f7L6HiIgI3L9/H/r6+sWeJycnBw8ePEB6ejoSExPxwQcfqOZpaGiga9eupS5jUgoPD0e9evXK/OT9be7fv4+srCz07du32PS8vDx06tQJABAdHV2sDgBwdHRUex1v8+uvv2LLli2Ij49HdnY28vLy0LFjx2LLdOjQAQ0aNCi23oyMDDx+/BgZGRnvrL2kqVOnYsKECQgKCkKfPn0wdOhQvP/++5XOwhhjrGZwA8EYk4xevXph/fr10NTUhJGRETQ0iu/idHV1iz3OyMhAly5dsHPnzlLP1axZswrVoLwkqTwyMjIAAIGBgTA2Ni42T0tLq0J1qGP37t2YNWsWVqxYAUdHR+jr62PZsmW4cuWK2s9RkdrHjRuH/v37IzAwEEFBQfD398eKFSswZcqUiodhjDFWY7iBYIxJhq6uLqysrNRevnPnztizZw+aN28OAwODMpdp1aoVrly5gu7duwMACgoKcO3aNXTu3LnM5e3t7aFQKBAcHIw+ffqUmq88A1JYWKiaZmdnBy0tLcTHx7/1zIWtra3qhnCly5cvvzvkP/jrr7/QrVs3TJw4UTXtwYMHpZaLiIhAdna2qjm6fPky9PT0YGpqCkNDw3fWXhZTU1N4eXnBy8sL3t7e2LhxIzcQjDEmEjwKE2Oszho5ciSaNm2KwYMH48KFC3j06BHOnz+PqVOn4smTJwCAadOmYenSpQgICMCdO3cwceLEf/wOBwsLC4wePRpff/01AgICVM+5d+9eAIC5uTlkMhmOHTuGlJQUZGRkQF9fH7NmzcJ3332Hbdu24cGDB7h+/TrWrFmDbdu2AQC8vLxw7949/N///R/u3r2LXbt2YevWrWrlTEhIQHh4eLGfly9fwtraGmFhYTh16hRiYmKwYMECXL16tdTv5+XlwdPTE1FRUTh+/Dh8fHwwefJkyOVytWovafr06Th16hQePXqE69ev49y5c7C1tVUrC2OMMeFxA8EYq7MaNGiAkJAQmJmZYciQIbC1tYWnpydycnJUZyRmzpyJr776CqNHj1Zd5uPh4fGPz7t+/Xp8+umnmDhxItq2bYtvvvkGmZmZAABjY2P4+flh7ty5aNGiBSZPngwAWLRoERYsWAB/f3/Y2trC1dUVgYGBsLS0BACYmZnhwIEDCAgIQIcOHbBhwwYsWbJErZzLly9Hp06div0EBgZi/PjxGDJkCIYPH44PPvgAqampxc5GKH300UewtrZG9+7dMXz4cAwaNKjYvSXvqr2kwsJCTJo0SbVsmzZtsG7dOrWyMMYYE56M3nYnIGOMMcYYY4yVwGcgGGOMMcYYY2rjBoIxxhhjjDGmNm4gGGOMMcYYY2rjBoIxxhhjjDGmNm4gGGOMMcYYY2rjBoIxxhhjjDGmNm4gGGOMMcYYY2rjBoIxxhhjjDGmNm4gGGOMMcYYY2rjBoIxxhhjjDGmNm4gGGOMMcYYY2rjBoIxxhhjjDGmtv8HlsqUmRB+RUkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Define your actual and predicted labels\n",
    "actual_labels = [2, 5, 9, 1, 7, 3, 8, 1, 2, 7, 6, 1, 5, 9, 8, 6, 4, 3, 0, 4]\n",
    "predicted_labels = [2, 3, 9, 1, 7, 3, 8, 1, 2, 7, 6, 1, 5, 9, 8, 6, 4, 3, 0, 4]\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(actual_labels, predicted_labels)\n",
    "\n",
    "# Plot the confusion matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "\n",
    "# Customize the plot\n",
    "class_names = ['Class 0', 'Class 1', 'Class 2', 'Class 3', 'Class 4', 'Class 5', 'Class 6', 'Class 7', 'Class 8', 'Class 9', 'Class 10', 'Class 11']\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names, rotation=45)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('Actual Labels')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b96e7002",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5835/2298656708.py:25: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  cbar.set_ticklabels(['0%', '20%', '40%', '60%', '85%', '100%'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Define your actual and predicted labels\n",
    "actual_labels = [2, 5, 9, 1, 7, 3, 8, 1, 2, 7, 6, 1, 5, 9, 8, 6, 4, 3, 0, 4]\n",
    "predicted_labels = [2, 3, 9, 1, 7, 3, 8, 1, 2, 7, 6, 1, 5, 9, 8, 6, 4, 3, 0, 4]\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(actual_labels, predicted_labels)\n",
    "\n",
    "# Calculate percentages\n",
    "cm_percent = np.round(cm / cm.sum(axis=1)[:, np.newaxis] * 100, 2)\n",
    "\n",
    "# Plot the confusion matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "ax = sns.heatmap(cm_percent, annot=True, fmt='.2f', cmap='Blues')\n",
    "\n",
    "# Customize the plot\n",
    "class_names = ['Class 0', 'Class 1', 'Class 2', 'Class 3', 'Class 4', 'Class 5', 'Class 6', 'Class 7', 'Class 8', 'Class 9', 'Class 10', 'Class 11']\n",
    "tick_marks = np.arange(len(class_names))\n",
    "cbar = ax.collections[0].colorbar\n",
    "# cbar.set_ticks([0, .2, .75, 1])\n",
    "cbar.set_ticklabels(['0%', '20%', '40%', '60%', '85%', '100%'])\n",
    "plt.xticks(tick_marks, class_names, rotation=45)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('Actual Labels')\n",
    "plt.title('Confusion Matrix (%)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5894434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Define your actual and predicted labels\n",
    "actual_labels = [0, 1, 0, 0, 1, 1, 0, 1, 1, 1]\n",
    "predicted_labels = [0, 1, 0, 0, 0, 1, 1, 1, 1, 1]\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(actual_labels, predicted_labels)\n",
    "\n",
    "# Create labels for the matrix\n",
    "labels = ['True Negative', 'False Positive', 'False Negative', 'True Positive']\n",
    "\n",
    "# Plot the confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('Actual Labels')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c6996da0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Define your actual and predicted labels\n",
    "actual_labels = [0, 1, 0, 0, 1, 1, 0, 1, 1, 1]\n",
    "predicted_labels = [0, 1, 0, 0, 0, 1, 1, 1, 1, 1]\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(actual_labels, predicted_labels)\n",
    "\n",
    "# Calculate percentages\n",
    "cm_percent = np.round(cm / np.sum(cm) * 100, 2)\n",
    "\n",
    "# Create labels for the matrix\n",
    "labels = ['True Negative', 'False Positive', 'False Negative', 'True Positive']\n",
    "\n",
    "# Plot the confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm_percent, annot=True, fmt='.2f', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('Actual Labels')\n",
    "plt.title('Confusion Matrix (%)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78ef20e5-709b-41bc-819b-d052d608108f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# -- Built-in modules -- #\n",
    "import os\n",
    "os.environ['AI4ARCTIC_DATA'] = ''  # Fill in directory for data location.\n",
    "os.environ['AI4ARCTIC_ENV'] = ''  # Fill in directory for environment with Ai4Arctic get-started package. \n",
    "\n",
    "\n",
    "# -- Third-part modules -- #\n",
    "import xarray as xr\n",
    "\n",
    "scene_name = '20180325T194759_dmi_prep.nc'  # Scene from the challenge dataset manual. Change as you like.\n",
    "scene = xr.open_dataset(os.path.join(os.environ['AI4ARCTIC_DATA'], scene_name))  # Open the scene.\n",
    "scene  # Enable interactive exploration of the scene (below)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdc6899c-827e-423f-8283-29bdcee26a4e",
   "metadata": {},
   "source": [
    "- Sea ice chart parameters\n",
    "    * 'SIC': The total SIC is the percentage ratio of sea ice to open-water for an area, discretised into 11 10%-bin classes ranging from 0% (open-water) to 100% (fully-covered sea ice). \n",
    "    * 'SOD': The SOD can also be viewed as the type of sea ice, which is a proxy for the thickness of the sea ice, i.e. how easy it is to traverse it. The parameter contains 5 classes, 0: Open-water, 1: New ice, 2: Young ice, 3: Thin First-year ice, 4: Thick First-year ice and 5: Old ice, i.e. older than 1 year.\n",
    "    * 'FLOE': The floe size describe how large or continuous the sea ice pieces/chunks are, with 6 parameters: 0: Open-water, 1: Cake ice, 2: Small floe, 3: Medium floe, 4: Big floe, 5: Vast floe, and 6: Bergs, i.e. variants of icebergs and glacier ice.\n",
    "- Geographical information\n",
    "    * 'distance_map': A map over the distance from land for all Sentinel-1 pixels. A nonlinear translation from kilometers to land to an index has been carried out.\n",
    "    * 'sar_2dgrid_latitude': Latitude of subgrid points in the scene (i.e. latitude/longitude per SAR pixel is not available).\n",
    "    * 'sar_2dgrid_longitude': Longitude of subgrid points in the scene.\n",
    "- Synthetic Aperture Radar\n",
    "    * 'nersc_sar_primary': HH Polarization\n",
    "    * 'nersc_sar_secondary': HV Polarization\n",
    "    * 'sar_incidenceangle': inclination of the measurement angle from the instrument to the ground\n",
    "- AMSR2 Passive Microwave Radiometer\n",
    "    * 'btemp_6_9v': 6.9 GHz vertical polarization. Transferred to the Sentinel-1 image geometry.\n",
    "    * 'btemp_6_9h': 6.9 GHz horizontal polarization.\n",
    "    * ..\n",
    "    * 'btemp_89h_0' : 89 GHz horizontal polarization.\n",
    "- ERA5 Environmental Variables\n",
    "    * 'u10m_rotated': ERA5 eastward component of the 10m wind rotated to account for the Sentinel-1 flight direction.\n",
    "    * 'v10m_rotated': ERA5 northward component of the 10m wind rotated to account for the Sentinel-1 flight direction.\n",
    "    * 't2m': ERA5 2m air temperature.\n",
    "    * 'skt': ERA5 skin temperature.\n",
    "    * 'tcwv': ERA5 total column water vapour.\n",
    "    * 'tclw': ERA5 total column cloud liquid water. \n",
    "\n",
    "There is no requirement to use all the data variables.\n",
    "There are three data grids to keep in mind in the dataset. The full grid containing the ice charts, distance map, SAR and auxiliary SAR variables. In connection to the SAR data, there is a grid with Ground Control Point (GCP) e.g. containing geographical location. Finally, there is the subgrid containing the AMSR2 and ERA5 environmental data.\n",
    "\n",
    "## Preprocessing steps.\n",
    "In the raw dataset, ice charts are stored in a 2darray containing ID numbers for polygons with an associated lookup table with ice information for each polygon. A polygon icechart conversion script is attached to do a similar conversion for the raw dataset. As the SOD and FLOE codes are given for partial sea ice concentrations, we utilize a threshold of 65% to indicate when a class is dominant in a polygon.\n",
    "\n",
    "To reduce the barrier of entry and ease of working with the data, the original 40 m pixel spacing ($\\sim$10,000 x 10,000 pixels) in the SAR (and charts etc.) data have been downsampled to 80 m ($\\sim$5,000 x 5,000 pixels). It is also required to deliver icecharts in this pixel spacing. The SAR, distance map and incidence angle have been downsampled using a 2×2 averaging kernel, whereas ice charts are reduced with a 2x2 max kernel. This is followed by aligning the masks (nan-values) across the data (except the subgridded variables). Afterwards, the scenes are standard scaled using the mean and standard deviation of the values for each variable (z = (x - u) / s, where x are original values, u = mean and s = standard deviation). Minimum and maximum, and mean and std values for each variable are also available in the two files 'global_minmax.npy' and 'global_meanstd.npy', respectively.\n",
    "\n",
    "Finally, NaN values in the SAR images are replaced with 0 and 255 in ice charts (stored as the largest value in uint8) to represent non-data or masked pixels. The chart fill value can be used to discount the relevant pixels during the loss calculation. Feel free to change these values or schemes. Below is an illustration of the different dataset parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14853919-2646-4829-8e7e-b1dfedefd799",
   "metadata": {},
   "source": [
    "# Sea ice charts\n",
    "Manual ice charting from multi-sensor satellite data analysis has for many years been the method applied at the National Ice Centers around the world for producing sea ice information for marine safety. Ice analysts primarily use SAR imagery due to the radar sensor's high resolution and capability to see through clouds and in polar darkness. The ice charts used in the AI4Arctic sea ice challenge dataset are from the Canadian (CIS)- and DMI operational ice services. The ice charts from the two ice services cover sub-regions of the Canadian and Greenland Waters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "890a7c0e-1b17-433c-91dd-3c640535a959",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# -- Third-part modules -- #\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# --Proprietary modules -- #\n",
    "from functions import chart_cbar\n",
    "from utils import CHARTS, GROUP_NAMES, LOOKUP_NAMES, SIC_LOOKUP, SOD_LOOKUP, FLOE_LOOKUP, SCENE_VARIABLES\n",
    "\n",
    "\n",
    "# - Show charts.\n",
    "fig, axs = plt.subplots(nrows=1, ncols=3, figsize=(12, 8))\n",
    "for idx, chart in enumerate(CHARTS):\n",
    "    scene[chart] = scene[chart].astype(float)  # Convert charts from uint8 to float to enable nans in the arrays, \n",
    "    # replace chart fill values with nan for better visualization.\n",
    "    scene[chart].values[scene[chart] == scene[chart].attrs['chart_fill_value']] = np.nan\n",
    "    axs[idx].imshow(scene[chart].values, vmin=0, vmax=LOOKUP_NAMES[chart]['n_classes'] - 2, cmap='jet', interpolation='nearest')\n",
    "    chart_cbar(ax=axs[idx], n_classes=LOOKUP_NAMES[chart]['n_classes'], chart=chart, cmap='jet')  # Creates colorbar with ice class names.\n",
    "\n",
    "plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0.75, hspace=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb621043-3e73-4605-b658-99eaec59e8f5",
   "metadata": {},
   "source": [
    "Gaps in the reference chart parameters can result from ice polygons either with no information on the parameter or if there is no dominant class meeting the threshold."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9599a99a-7891-4ede-8163-2984c1e407ef",
   "metadata": {},
   "source": [
    "# Synthetic Aperture Radar data and auxiliary variables\n",
    "Sentinel-1 is the radar mission of the Copernicus Earth Observation programme of the European Union (EU). The Sentinel-1 mission comprises a constellation of two polar-orbiting satellites, Sentinel-1A and Sentinel-1B, sharing the same orbital plane and collecting C-band (4.5 cm wavelength) synthetic aperture radar (SAR) images. Radar images can be acquired regardless of the weather. The Sentinel-1 SAR has the advantage of operating at a wavelength not impeded by cloud cover or a lack of illumination, such as in polar darkness, and can acquire data over a site during day or night time under all weather conditions. Since the Arctic area is dominated by cloud cover and polar darkness for a large part of the year, the SAR instrument has for many years been valuable for Arctic monitoring applications such as sea ice charting.\n",
    "The Sentinel-1 sensor transmits a radar signal towards the ground, and the backscatter is the portion of the outgoing radar signal that the target on the Earth’s surface redirects directly back towards the radar antenna. Some portions of the incident radar signal will be reflected and/or scattered away from the radar or absorbed.\n",
    "\n",
    "The mode and polarization specifications of the Sentinel-1 images in the sea ice dataset are among those that are traditionally used for ice charting; Sentinel-1 Extra Wide Swath Mode (EW) Level-1 Ground Range Detected products in Medium resolution (GRDM) and in dual polarization, HH and HV. These Sentinel-1 image products cover 400 x 400 square kilometres. A Sentinel-1 image in EW mode consists of five sub-swaths. There are some radiometric variations between these sub-swaths, most evident in HV polarization images. Correcting these differences is a complex task. Other phenomena, such as scalloping effects, are also present in some images. The SAR noise correction in this dataset is provided by the NERSC (Korosov et al., 2021).\n",
    "\n",
    "It is common practice to visualize SAR data using a grey colourmap. To improve the contrast, 5 and 95% quantiles are used. The incidence angle and distance map are straightforward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9428b22-33ae-42b8-97ec-47426824556b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show full resolution variables.\n",
    "fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(14,14))\n",
    "for idx, full_variable in enumerate(SCENE_VARIABLES[0:4]):\n",
    "    ax = axs[idx // 2, idx % 2]\n",
    "    if 'nersc_sar' in full_variable:\n",
    "        scene[full_variable].values[scene[full_variable].values == scene[full_variable].attrs['variable_fill_value']] = np.nan\n",
    "        label = 'Scaled backscatter'\n",
    "        im = ax.imshow(scene[full_variable].values, cmap='gray',\n",
    "                       vmin=np.nanquantile(scene[full_variable].values, q=0.025),\n",
    "                       vmax=np.nanquantile(scene[full_variable].values, q=0.975))\n",
    "    elif 'incidenceangle' in full_variable:\n",
    "        scene[full_variable].values[scene[full_variable].values == scene[full_variable].attrs['variable_fill_value']] = np.nan\n",
    "        label = 'Incidence angle'\n",
    "        im = ax.imshow(scene[full_variable].values)\n",
    "    elif 'map' in full_variable:\n",
    "        label = 'Distance to land'\n",
    "        im = ax.imshow(scene[full_variable].values)\n",
    "    plt.colorbar(im, ax=ax, fraction=0.0485, pad=0.049, label=label)\n",
    "    \n",
    "    \n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdae9187-1d80-4031-9882-9faeb0122960",
   "metadata": {},
   "source": [
    "# AMSR2 channels\n",
    "For each Sentinel-1 scene, a corresponding AMSR2 part of the netCDF file is produced, containing the AMSR2 brightness temperature pixels that are transferred to the Sentinel-1 image geometry. The AMSR2 swaths are resampled to the coordinates of every 50 x 50 (2 km) Sentinel-1 pixel due to the much coarser resolution of the AMSR2 data. Data in the AMSR2 part of the netCDF file are brightness temperatures for each polarization and frequency available from the AMSR2 sensor.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3710add2-cacb-45fc-8f04-70e1a644f030",
   "metadata": {},
   "outputs": [],
   "source": [
    "# There is no mask in these scene variables.\n",
    "fig, axs = plt.subplots(nrows=4, ncols=4, figsize=(10,10))\n",
    "for idx, amsr2_channel in enumerate(SCENE_VARIABLES[4:17]):\n",
    "    ax = axs[idx // 4, idx % 4]\n",
    "    im = ax.imshow(scene[amsr2_channel])\n",
    "    plt.colorbar(im, ax=ax, fraction=0.0485, pad=0.049, label=amsr2_channel)\n",
    "\n",
    "fig.suptitle('AMSR2 Brightness temperature, 6.9-89 GHz vertical and horizontal polarization', y=0.875)\n",
    "[axs[-1, col].axis('off') for col in range(1, 4)]\n",
    "plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0.9, hspace=-0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e3dfa2-9f53-4957-bc85-18bc36a5b198",
   "metadata": {},
   "source": [
    "#  Environmental variables\n",
    "For each Sentinel-1 scene, a corresponding ERA5 part of the netCDF file is produced, containing several NWP parameters. These parameters are resampled in the same manner as the AMSR2 brightness temperatures. The ERA5 data is retrieved from the ERA5 hourly data on single levels from 1959 to present reanalysis dataset, which is available at the Copernicus Climate Data Store (cds.climate.copernicus.eu). For each Sentinel-1 scene the NWP parameters with the smallest difference in time to the Sentinel-1 acquisition time are retrieved and resampled to the Sentinel-1 pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05676f5a-09a1-4b47-b0f3-6318b400a5f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# There is no mask in these scene variables.\n",
    "fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(10,10))\n",
    "for idx, env_var in enumerate(SCENE_VARIABLES[18:24]):\n",
    "    ax = axs[idx // 3, idx % 3]\n",
    "    im = ax.imshow(scene[env_var])\n",
    "    plt.colorbar(im, ax=ax, fraction=0.0485, pad=0.049, label=env_var + ' ' + scene[env_var].units)\n",
    "\n",
    "fig.suptitle('Environmental variables', y=0.8)\n",
    "plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0.5, hspace=-0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0766a928-7d79-486f-b0c5-b7e190226f62",
   "metadata": {},
   "source": [
    "# Data statistics\n",
    "In the folder 'misc', statistical information regarding the data is available. Class bins for each chart in every (ready-to-train) scene, as well as means, standard deviations, and the minimum and maximum values of the raw challenge dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e57b556e-f042-4aff-988e-4969523ce81f",
   "metadata": {},
   "outputs": [],
   "source": [
    "global_meanstd = np.load(os.environ['AI4ARCTIC_ENV'] + '/misc/global_meanstd.npy', allow_pickle=True).item()\n",
    "global_minmax = np.load(os.environ['AI4ARCTIC_ENV'] + '/misc/global_minmax.npy', allow_pickle=True).item()\n",
    "\n",
    "for variable in SCENE_VARIABLES:\n",
    "    print(f\"{variable}; min: {global_minmax[variable]['min']:.3f}, mean: {global_meanstd[variable]['mean']:.3f}, \"\\\n",
    "    f\"max: {global_minmax[variable]['max']:.3f}, std: {global_meanstd[variable]['std']:.3f}\")"
   ]
  }
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